A multistate framework for the analysis of subsequent injury in sport (M-FASIS)

Investigating the Effect of Return-to-Play Timing After Injury on Performance: Does the Analysis Answer the Research Objective?

Return-to-play decision making should be based on all the advantages and disadvantages of return to play for athletes, not just the risk of injury. For competitive athletes, this includes the effect of early versus delayed return to sport on performance. In this paper, we address the questions "How can I estimate the effect of injury on the individual's performance at return to play?" and "What is the effect of delaying return to sport on the individual's performance?". To address these questions, we describe (1) some foundational concepts, design and analytical challenges related to estimating the causal effect of return to play timing on performance in the athlete, (2) additional challenges if one is interested in the effects of delaying return to play and (3) differences when the questions relate to the team's performance. Although the analytical strategies described appear complicated, coaches and athletes make these judgements informally every day without explicitly stating their assumptions. Using a formal approach should help analysts provide the most valid answers to the questions asked by athletes and coaches. In brief, the choice of a comparison group depends on the research question and requires that one consider the hypothetical performance trajectory of the athlete had they never been injured. Thus, the optimal comparison group depends on the shape of the expected trajectory and the specific research question being asked.

Observed Injury Rates Did Not Follow Theoretically Predicted Injury Risk Patterns in Professional Human Circus Artists

OBJECTIVE

Identifying which types of athletes have increased injury risk (ie, predictive risk factors) should help develop cost-effective selective injury prevention strategies. Our objective was to compare a theoretical injury risk classification system developed by coaches and rehabilitation therapists, with observed injury rates in human circus acts across dimensions of physical stressors, acrobatic complexity, qualifications, and residual risks.

DESIGN

Descriptive epidemiological study.

SETTING

professional circus company.

PATIENTS OR OTHER PARTICIPANTS

Human circus artists performing in routine roles between 2007 and 2017.

ASSESSMENT OF RISK FACTORS

Characteristics of circus acts categorized according to 4 different dimensions.

MAIN OUTCOME MEASURES

Medical attention injury rates (injury requiring a visit to the therapist), time-loss injury rates (TL-1; injury resulting in at least one missed performance), and time-loss 15 injury rates (TL-15; injury resulting in at least 15 missed performances).

RESULTS

Among 962 artists with 1 373 572 performances, 89.4% (860/962) incurred at least one medical attention injury, 74.2% (714/962) incurred at least one TL-1 injury, and 50.8% (489/962) incurred at least one TL-15 injury. There were important inconsistencies between theoretical and observed injury risk patterns in each of the 4 dimensions for all injury definitions (medical attention, TL-1, and TL-15).

CONCLUSIONS

Although theoretical classifications are the only option when no data are available, observed risk patterns based on injury surveillance programs can help identify artists who have a high (or low) theoretical risk but are nonetheless actually at low (or high) risk of injury, given their current roles. This will help develop more cost-effective selective injury prevention programs.

Evolving Expectations of the Orthopedic Team Physician: Managing the Sidelines and Landmines

ABSTRACT

The role of orthopedic team physicians has evolved greatly over the past decade having been influenced by advances in sports science and performance, new surgical and biologic technologies, social media, medicolegal liability, marketing, and sexual misconduct cases by some team physicians. The great variety of events and sports that are covered from high school and collegiate to the Olympic and professional levels requires a myriad of skills outside of the traditional medical training curriculum. In the current climate of increasing media scrutiny from a 24-h news cycle it is imperative for orthopedic team physicians, whether operative or nonoperative, to continually adapt to the needs and expectations of athletes who also are patients. This is especially true in the wake of the COVID-19 pandemic. Orthopedic team physicians' responsibilities continue to evolve ensuring their relevance and necessity on the sidelines and in the training room as well as in the operative suite.

Lower limb MSK injuries among school-aged rugby and football players: a systematic review

Objective

The objective of this systematic review was to explore the incidence of lower limb musculoskeletal (MSK) injuries sustained by rugby union, rugby league, soccer, Australian Rules and Gaelic football players under 18 years. The review sought to identify the mechanisms and types of injury sustained and to compare between sports.

Design

This systematic review focused on the incidence of lower limb injury in adolescent team sports that involved running and kicking a ball. A literature search of studies published prior to January 2020 was conducted using SportDiscus, Medline and PubMed databases. The Standard Quality Assessment Criteria appraisal tool was used to assess the quality of each article included in the review. Two or more authors independently reviewed all papers.

Results

Sixteen papers met the inclusion criteria; prospective cohort (N=14), retrospective (n=1) and longitudinal (n=1). These studies investigated injuries in rugby union and rugby league (n=10), football (soccer) (n=3), Australian Rules (n=2) and Gaelic football (n=1). There were a total of 55 882 participants, aged 7–19 years old, who reported 6525 injuries. The type, site and mechanisms of injury differed across sports.

Summary

Lower limb injuries were common in adolescent rugby, soccer, Gaelic football and Australian Rules football players, however these studies may not fully reflect the true injury burden where recurrent and overuse injuries have not been considered. There were differences between sports in the mechanisms, types and severity of injury.

Consensus statements that fail to recognise dissent are flawed by design: a narrative review with 10 suggested improvements

Consensus statements have the potential to be very influential. Recently, such statements in sport and exercise medicine appear more prescriptive, strongly recommending particular approaches to research or treatment. In 2020, a statement on methods for reporting sport injury surveillance studies included an extension to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines; STROBE guidelines are now official requirements for many journals. This suggests that investigators who use methods outside of these guidelines may have difficulty publishing their results. By definition, consensus is not unanimity, and consensus recommendations are sometimes considered flawed at a later date. This is expected as a discipline benefits from new knowledge. However, the consensus methods themselves may also inadvertently suppress contrary-but valid-opinions. I point to a different model for consensus meetings and statements that embraces dissenting opinions and is more transparent than common current methods in sport and exercise medicine. The method, based on how Supreme Courts function in many countries, allows for both majority and one or more minority opinions. I illustrate how a consensus statement might be written using examples from four previous sport and exercise medicine consensus statements. By adopting the 'Supreme Court' approach, important disagreements about the strength and interpretation of evidence will be far more visible than is currently the case in most consensus meetings. The benefit of the Supreme Court model is that it will ensure that clinicians, researchers and journals are not inappropriately influenced by recommendations from consensus statements where uncertainty remains.

Statement on methods in sport injury research from the 1st METHODS MATTER Meeting, Copenhagen, 2019

High quality sports injury research can facilitate sports injury prevention and treatment. There is scope to improve how our field applies best practice methods—methods matter (greatly!). The 1st METHODS MATTER Meeting, held in January 2019 in Copenhagen, Denmark, was the forum for an international group of researchers with expertise in research methods to discuss sports injury methods. We discussed important epidemiological and statistical topics within the field of sports injury research. With this opinion document, we provide the main take-home messages that emerged from the meeting.

Statement on Methods in Sport Injury Research From the First METHODS MATTER Meeting, Copenhagen, 2019

High-quality sports injury research can facilitate sports injury prevention and treatment. There is scope to improve how our field applies best-practice methods-methods matter (greatly!). The first METHODS MATTER meeting, held in January 2019 in Copenhagen, Denmark, was the forum for an international group of researchers with expertise in research methods to discuss sports injury methods. We discussed important epidemiological and statistical topics within the field of sports injury research. With this opinion document, we provide the main take-home messages that emerged from the meeting. Meeting participants agreed that the definition of sport injury depends on the research question and context. It was considered essential to be explicit about the goal of the research effort and to use frameworks to illustrate the assumptions that underpin measurement and the analytical strategy. Complex systems were discussed to illustrate how potential risk factors can interact in a nonlinear way. This approach is often a useful alternative to identifying single risk factors. Investigating changes in exposure status over time is important when analyzing sport injury etiology, and analyzing recurrent injury, subsequent injury, or injury exacerbation remains challenging. The choice of statistical model should consider the research question, injury measure (eg, prevalence, incidence), type and granularity of injury data (categorical or continuous), and study design. Multidisciplinary collaboration will be a cornerstone for future high-quality sport injury research. Working outside professional silos in a diverse, multidisciplinary team benefits the research process, from the formulation of research questions and designs to the statistical analyses and dissemination of study results in implementation contexts. This article has been copublished in the British Journal of Sports Medicine and the Journal of Orthopaedic & Sports Physical Therapy. J Orthop Sports Phys Ther 2020;50(5):226-233. doi:10.2519/jospt.2020.9876.

The Garmin-RUNSAFE Running Health Study on the aetiology of running-related injuries: rationale and design of an 18-month prospective cohort study including runners worldwide

INTRODUCTION

Running injuries affect millions of persons every year and have become a substantial public health issue owing to the popularity of running. To ensure adherence to running, it is important to prevent injuries and to have an in-depth understanding of the aetiology of running injuries. The main purpose of the present paper was to describe the design of a future prospective cohort study exploring if a dose-response relationship exists between changes in training load and running injury occurrence, and how this association is modified by other variables.

METHODS AND ANALYSIS

In this protocol, the design of an 18-month observational prospective cohort study is described that will include a minimum of 20 000 consenting runners who upload their running data to Garmin Connect and volunteer to be a part of the study. The primary outcome is running-related injuries categorised into the following states: (1) no injury; (2) a problem; and (3) injury. The primary exposure is change in training load (eg, running distance and the cumulative training load based on the number of strides, ground contact time, vertical oscillation and body weight). The change in training load is a time-dependent exposure in the sense that progression or regression can change many times during follow-up. Effect-measure modifiers include, but is not limited to, other types of sports activity, activity of daily living and demographics, and are assessed through questionnaires and/or by Garmin devices.

ETHICS AND DISSEMINATION

The study design, procedures and informed consent have been evaluated by the Ethics Committee of the Central Denmark Region (Request number: 227/2016 - Record number: 1-10-72-189-16).

Subsequent Injury Risk Is Elevated Above Baseline After Return to Play: A 5-Year Prospective Study in Elite Australian Football

BACKGROUND

The risk of sustaining a subsequent injury is elevated in the weeks after return to play (RTP) from an index injury. However, little is known about the magnitude, duration, and nature by which subsequent injury risk is increased.

PURPOSE

To quantify and describe the risk of injury in a 12-week period after RTP from an index injury in Australian football players.

STUDY DESIGN

Cohort study; Level of evidence, 2.

METHODS

Injury data were collected from 79 players over 5 years at 1 Australian Football League club. Injuries were classified with the Orchard Sports Injury Classification System and by side of the body. Furthermore, injury severity was classified as time loss (resulting in ≥1 matches being missed) or non-time loss (no matches missed). Subsequent injury was categorized with the SIC-2.0 model and applied to the data set via an automated script. The probability of a time loss subsequent injury was calculated for in-season index injuries for each week of a 12-week period after RTP via a mixed effect logistic regression model.

RESULTS

Subsequent injury risk was found to be highest in the week of RTP for both time loss injuries (9.4%) and non-time loss injuries (6.9%). Risk decreased with each week survived after RTP; however, it did not return to baseline risk of participation (3.6%).

CONCLUSION

These findings demonstrate that athletes returning to play are at an increased risk of injury for a number of weeks, thus indicating the requirement for tertiary prevention strategies to ensure that they survive this period.

The Establishment and Refinement of the National Basketball Association Player Injury and Illness Database

The National Basketball Association (NBA; also referred to as "the league") has established a centralized, audited electronic medical record system that has been linked with external sources to provide a platform for robust research and to allow the NBA to conduct player health and safety reviews. The system is customized and maintained by the NBA and individual teams as part of the employment records for each player and is deployed uniformly across all 30 teams in the league, thereby allowing for standardized data on injuries, illnesses, and player participation in NBA games and practices. The electronic medical record data are enriched by linkage with other league external data sources that provide additional information about injuries, players, game and practice participation, and movement. These data linkages allow for the assessment of potential injury trends, development of injury-prevention programs, and rule changes, with the ultimate goal of improving player health and wellness. The purpose of this article is to describe this NBA injury database, including the details of data collection, data linkages with external data sources, and activities related to reporter training and data quality improvement.

Time-to-event analysis for sports injury research part 1: time-varying exposures

BACKGROUND

'How much change in training load is too much before injury is sustained, among different athletes?' is a key question in sports medicine and sports science. To address this question the investigator/practitioner must analyse exposure variables that change over time, such as change in training load. Very few studies have included time-varying exposures (eg, training load) and time-varying effect-measure modifiers (eg, previous injury, biomechanics, sleep/stress) when studying sports injury aetiology.

AIM

To discuss advanced statistical methods suitable for the complex analysis of time-varying exposures such as changes in training load and injury-related outcomes.

CONTENT

Time-varying exposures and time-varying effect-measure modifiers can be used in time-to-event models to investigate sport injury aetiology. We address four key-questions (i) Does time-to-event modelling allow change in training load to be included as a time-varying exposure for sport injury development? (ii) Why is time-to-event analysis superior to other analytical concepts when analysing training-load related data that changes status over time? (iii) How can researchers include change in training load in a time-to-event analysis? and, (iv) Are researchers able to include other time-varying variables into time-to-event analyses? We emphasise that cleaning datasets, setting up the data, performing analyses with time-varying variables and interpreting the results is time-consuming, and requires dedication. It may need you to ask for assistance from methodological peers as the analytical approaches presented this paper require specialist knowledge and well-honed statistical skills.

CONCLUSION

To increase knowledge about the association between changes in training load and injury, we encourage sports injury researchers to collaborate with statisticians and/or methodological epidemiologists to carefully consider applying time-to-event models to prospective sports injury data. This will ensure appropriate interpretation of time-to-event data.

An Updated Subsequent Injury Categorisation Model (SIC-2.0): Data-Driven Categorisation of Subsequent Injuries in Sport

BACKGROUND

Accounting for subsequent injuries is critical for sports injury epidemiology. The subsequent injury categorisation (SIC-1.0) model was developed to create a framework for accurate categorisation of subsequent injuries but its operationalisation has been challenging.

OBJECTIVES

The objective of this study was to update the subsequent injury categorisation (SIC-1.0 to SIC-2.0) model to improve its utility and application to sports injury datasets, and to test its applicability to a sports injury dataset.

METHODS

The SIC-1.0 model was expanded to include two levels of categorisation describing how previous injuries relate to subsequent events. A data-driven classification level was established containing eight discrete injury categories identifiable without clinical input. A sequential classification level that sub-categorised the data-driven categories according to their level of clinical relatedness has 16 distinct subsequent injury types. Manual and automated SIC-2.0 model categorisation were applied to a prospective injury dataset collected for elite rugby sevens players over a 2-year period. Absolute agreement between the two coding methods was assessed.

RESULTS

An automated script for automatic data-driven categorisation and a flowchart for manual coding were developed for the SIC-2.0 model. The SIC-2.0 model was applied to 246 injuries sustained by 55 players (median four injuries, range 1-12), 46 (83.6%) of whom experienced more than one injury. The majority of subsequent injuries (78.7%) were sustained to a different site and were of a different nature. Absolute agreement between the manual coding and automated statistical script category allocation was 100%.

CONCLUSIONS

The updated SIC-2.0 model provides a simple flowchart and automated electronic script to allow both an accurate and efficient method of categorising subsequent injury data in sport.

Time-to-event analysis for sports injury research part 2: time-varying outcomes

BACKGROUND

Time-to-event modelling is underutilised in sports injury research. Still, sports injury researchers have been encouraged to consider time-to-event analyses as a powerful alternative to other statistical methods. Therefore, it is important to shed light on statistical approaches suitable for analysing training load related key-questions within the sports injury domain.

CONTENT

In the present article, we illuminate: (i) the possibilities of including time-varying outcomes in time-to-event analyses, (ii) how to deal with a situation where different types of sports injuries are included in the analyses (ie, competing risks), and (iii) how to deal with the situation where multiple subsequent injuries occur in the same athlete.

CONCLUSION

Time-to-event analyses can handle time-varying outcomes, competing risk and multiple subsequent injuries. Although powerful, time-to-event has important requirements: researchers are encouraged to carefully consider prior to any data collection that five injuries per exposure state or transition is needed to avoid conducting statistical analyses on time-to-event data leading to biased results. This requirement becomes particularly difficult to accommodate when a stratified analysis is required as the number of variables increases exponentially for each additional strata included. In future sports injury research, we need stratified analyses if the target of our research is to respond to the question: 'how much change in training load is too much before injury is sustained, among athletes with different characteristics?' Responding to this question using multiple time-varying exposures (and outcomes) requires millions of injuries. This should not be a barrier for future research, but collaborations across borders to collecting the amount of data needed seems to be an important step forward.

Getting the most out of intensive longitudinal data: a methodological review of workload-injury studies

OBJECTIVES

To systematically identify and qualitatively review the statistical approaches used in prospective cohort studies of team sports that reported intensive longitudinal data (ILD) (>20 observations per athlete) and examined the relationship between athletic workloads and injuries. Since longitudinal research can be improved by aligning the (1) theoretical model, (2) temporal design and (3) statistical approach, we reviewed the statistical approaches used in these studies to evaluate how closely they aligned these three components.

DESIGN

Methodological review.

METHODS

After finding 6 systematic reviews and 1 consensus statement in our systematic search, we extracted 34 original prospective cohort studies of team sports that reported ILD (>20 observations per athlete) and examined the relationship between athletic workloads and injuries. Using Professor Linda Collins' three-part framework of aligning the theoretical model, temporal design and statistical approach, we qualitatively assessed how well the statistical approaches aligned with the intensive longitudinal nature of the data, and with the underlying theoretical model. Finally, we discussed the implications of each statistical approach and provide recommendations for future research.

RESULTS

Statistical methods such as correlations, t-tests and simple linear/logistic regression were commonly used. However, these methods did not adequately address the (1) themes of theoretical models underlying workloads and injury, nor the (2) temporal design challenges (ILD). Although time-to-event analyses (eg, Cox proportional hazards and frailty models) and multilevel modelling are better-suited for ILD, these were used in fewer than a 10% of the studies (n=3).

CONCLUSIONS

Rapidly accelerating availability of ILD is the norm in many fields of healthcare delivery and thus health research. These data present an opportunity to better address research questions, especially when appropriate statistical analyses are chosen.

Personal subjectivity in clinician discussion about retirement from sport post-concussion

BACKGROUND

There is increasing focus on the number of concussions after which an athlete should discontinue participating in contact or collision sports. This is a clinically subjective and, in some cases, a preference-sensitive decision.

PURPOSE

Our goal was to assess whether there is personal subjectivity in when athletic trainers (ATs) discuss the possibility of sport retirement post-concussion with athletes.

METHODS

A national sample of ATs who provide clinical care to college athletes completed a questionnaire (n = 677, 34% response rate). Structural equation modeling was used to assess the association between risk perceptions, risk tolerance, social influences (beliefs about athlete, coach and parent preferences), expectancies about athlete success (on and off field), and the number of concussions after which retirement would be discussed.

RESULTS

There was a significant direct effect of AT risk tolerance on the number of concussions after which sport retirement would be discussed, among both male and female ATs (male B = 0.54, p < 0.001; female B = 0.31, p < 0.001). ATs who more strongly prioritized athletes' on-field achievements indicated that they would discuss sport retirement after a larger number of concussions compared to their peers who less strongly valued athletic achievement.

CONCLUSIONS

The results suggest the potential utility of standardized approaches to communicating concussion risk consistently to athletes. This can help ensure that all athletes are uniformly able to make informed decisions about continued exposure to concussion risk that are based on their values and preferences.

The Epidemiology of Stress Fractures in Collegiate Student-Athletes, 2004-2005 Through 2013-2014 Academic Years

CONTEXT

  Stress fractures are injuries caused by cumulative, repetitive stress that leads to abnormal bone remodeling. Specific populations, including female athletes and endurance athletes, are at higher risk than the general athletic population. Whereas more than 460 000 individuals participate in collegiate athletics in the United States, no large study has been conducted to determine the incidence of stress fractures in collegiate athletes.

OBJECTIVE

  To assess the incidence of stress fractures in National Collegiate Athletic Association (NCAA) athletes and investigate rates and patterns overall and by sport.

DESIGN

  Descriptive epidemiology study.

SETTING

  National Collegiate Athletic Association institutions.

PATIENTS OR OTHER PARTICIPANTS

  National Collegiate Athletic Association athletes.

MAIN OUTCOME MEASURE(S)

  Data were analyzed from the NCAA Injury Surveillance Program for the academic years 2004-2005 through 2013-2014. We calculated rates and rate ratios (RRs) with 95% confidence intervals (CIs).

RESULTS

  A total of 671 stress fractures were reported over 11 778 145 athlete-exposures (AEs) for an overall injury rate of 5.70 per 100 000 AEs. The sports with the highest rates of stress fractures were women's cross-country ( 28.59/100  000 AEs), women's gymnastics ( 25.58/100  000 AEs), and women's outdoor track ( 22.26/100  000 AEs). Among sex-comparable sports (baseball/softball, basketball, cross-country, ice hockey, lacrosse, soccer, swimming and diving, tennis, indoor track, and outdoor track), stress fracture rates were higher in women (9.13/100 000 AEs) than in men (4.44/100 000 AEs; RR = 2.06; 95% CI = 1.71, 2.47). Overall, stress fracture rates for these NCAA athletes were higher in the preseason (7.30/100 000 AEs) than in the regular season (5.12/100 000 AEs; RR = 1.43; 95% CI = 1.22, 1.67). The metatarsals (n = 254, 37.9%), tibia (n = 147, 21.9%), and lower back/lumbar spine/pelvis (n = 81, 12.1%) were the most common locations of injury. Overall, 21.5% (n = 144) of stress fractures were recurrent injuries, and 20.7% (n = 139) were season-ending injuries.

CONCLUSIONS

  Women experienced stress fractures at higher rates than men, more often in the preseason, and predominantly in the foot and lower leg. Researchers should continue to investigate biological and biomechanical risk factors for these injuries as well as prevention interventions.

A framework for the etiology of running-related injuries

The etiology of running-related injury is important to consider as the effectiveness of a given running-related injury prevention intervention is dependent on whether etiologic factors are readily modifiable and consistent with a biologically plausible causal mechanism. Therefore, the purpose of the present article was to present an evidence-informed conceptual framework outlining the multifactorial nature of running-related injury etiology. In the framework, four mutually exclusive parts are presented: (a) Structure-specific capacity when entering a running session; (b) structure-specific cumulative load per running session; (c) reduction in the structure-specific capacity during a running session; and (d) exceeding the structure-specific capacity. The framework can then be used to inform the design of future running-related injury prevention studies, including the formation of research questions and hypotheses, as well as the monitoring of participation-related and non-participation-related exposures. In addition, future research applications should focus on addressing how changes in one or more exposures influence the risk of running-related injury. This necessitates the investigation of how different factors affect the structure-specific load and/or the load capacity, and the dose-response relationship between running participation and injury risk. Ultimately, this direction allows researchers to move beyond traditional risk factor identification to produce research findings that are not only reliably reported in terms of the observed cause-effect association, but also translatable in practice.

How should we categorise self-reported data on subsequent injuries?

Classifying subsequent injuries is of high importance in injury epidemiology since a previous injury has been reported to increase the risk of a new injury or increase the risk of a more severe injury. Multiple reports have shown that self-reported data provide an extensive view of an injury problem and add valuable information to the understanding of the athlete's health. The purpose of this study was to display a method that can be used to facilitate classification of subsequent injuries and to discuss challenges faced when categorising subsequent injuries based on self-reported data. The suitability of a new model for Subsequent Injuries Adjusted for Self-reported data (SIAS model) was demonstrated with sport injury data from a cohort of 101 adolescent elite track & field athletes, followed over 52 weeks. A total number of 71 subsequent injuries were identified. Of all subsequent injuries, recurrent injuries represented 69.0% (n = 49) and 31.0% (n = 22) were classified as new injuries. The majority of subsequent injuries (n = 60, 84.5%) occurred after athletes had recovered from a previous injury. Of all subsequent injuries, 15.5% (n = 11) represented injuries where athletes had not fully recovered from a previous injury. Application of the SIAS model allows for classification of subsequent injuries based on self-reported data on the recovery level of the athletes, the injury onset and injury type. The developed SIAS model follows the consensus recommendations of injury definition, injury classification and is an attempt to increase the understanding of the complex relationship of subsequent injuries in self-reported data sets.

Application of the subsequent injury categorisation model for longitudinal injury surveillance in elite rugby and cricket: intersport comparisons and inter-rater reliability of coding

BACKGROUND

When an athlete has more than one injury over a time period, it is important to determine if these are related to each other or not. The subsequent injury categorisation (SIC) model is a method designed to consider the relationship between an index injury and subsequent injury(ies).

OBJECTIVE

The primary aim was to apply SIC to longitudinal injury data from two team sports: rugby union and cricket. The secondary aim was to determine SIC inter-rater reliability.

METHODS

Rugby union (time-loss; TL) and cricket (TL and non-time-loss; NTL) injuries sustained between 2011 and 2014 within one international team, respectively, were recorded using international consensus methods. SIC was applied by multiple raters, team clinicians, non-team clinicians, and a sports scientist. Weighted kappa and Cohen's kappa scores were calculated for inter-rater reliability of the rugby union TL injuries and cricket NTL and TL injuries.

RESULTS

67% and 51% of the subsequent injuries in rugby union and cricket respectively were categorised as injuries to a different body part not related to an index injury (SIC code 10). At least moderate agreement (weighted and Cohen kappa ≥0.60) was observed for team clinicians and the non-team clinician for both sports. Including NTL and TL injuries increased agreement between team clinician and non-team clinician, but not between clinician and sports scientist.

CONCLUSION

The most common subsequent injury in both sports was an injury to a different body part that was not related to an index injury. The SIC model was generally reliable, with the highest agreement between clinicians working within the same team. Recommendations for future use of SIC are provided based on the proximity of the rater to the team and the raters' level of clinical knowledge.

A higher sport-related reinjury risk does not mean inadequate rehabilitation: the methodological challenge of choosing the correct comparison group

Previous injury is a well-established predictor of subsequent injury in sports medicine. Some have interpreted this to mean that either our current methods of rehabilitation are inadequate or there is some permanent damage to the tissue and 100% rehabilitation is not possible. In 2011, we illustrated that these analyses and interpretations failed to account for the fact that some athletes are more prone to get injured, either physiologically, or because of their role/type of play. We suggested that the appropriate analysis would simply require using statistical methods that measured how each individual athlete's risk changed from preinjury to postinjury.In this paper, we revisit our recommendation and illustrate that it too would be flawed if the risk of injury changed over time independent of an injury ever occurring. This might be expected if general fitness were to decline over the season, or if the style of play changed between early season games and postseason championship games. Acknowledging that risk may change regardless of whether an injury occurred or not leads to three different general definitions of 100% rehabilitation: (1) a return to the baseline state, (2) a return to the immediate preinjury state and (3) a return to the state that would have been present had the initial injury never occurred. We guide the reader on how to estimate the risks for each definition and the assumptions that must be acknowledged.