External validation of the POP score for predicting obstetric and gynecological diseases in the emergency department

Development and Validation of a Novel Method for Converting the Japan Coma Scale to Glasgow Coma Scale

BACKGROUND

The Japan Coma Scale (JCS) is the most frequently adopted method for evaluating level of consciousness in Japan. However, no validated method for converting the JCS to the Glasgow Coma Scale (GCS) exists. The aims of the present study were to develop and validate a method to convert the JCS to GCS.

METHODS

This is a multicenter retrospective analysis involving three emergency departments (EDs) in Japan. We included all adult patients who visited the ED between 2017 and 2020. The participating facilities were divided into two cohorts-one cohort to develop a table to convert the JCS to GCS (development cohort), and the other cohort to validate the conversion table (validation cohort). The conversion table of the JCS to GCS was developed based on the median values of the GCS. The outcome was the concordance rate between the JCS and GCS.

RESULTS

We identified 8,194 eligible patients. The development cohort included 7,373 patients and the validation cohort included 821 patients. In the validation cohort, the absolute and relative concordance rates were 80.3% (95% confidence interval, 77.4-82.9%) and 93.2% (95% confidence interval, 91.2-94.8%), respectively.

CONCLUSION

This study developed and validated a novel method for converting the JCS to GCS. Assuming the offset by a single category between the JCS and GCS is acceptable, the concordance rate was over 90% in the general adult patient population visiting the ED. The conversion method may assist researchers to convert JCS scores into GCS scores, which are more commonly recognized among global audiences.

AI and machine learning in resuscitation: Ongoing research, new concepts, and key challenges

Aim

Artificial intelligence (AI) and machine learning (ML) are important areas of computer science that have recently attracted attention for their application to medicine. However, as techniques continue to advance and become more complex, it is increasingly challenging for clinicians to stay abreast of the latest research. This overview aims to translate research concepts and potential concerns to healthcare professionals interested in applying AI and ML to resuscitation research but who are not experts in the field.

Main text

We present various research including prediction models using structured and unstructured data, exploring treatment heterogeneity, reinforcement learning, language processing, and large-scale language models. These studies potentially offer valuable insights for optimizing treatment strategies and clinical workflows. However, implementing AI and ML in clinical settings presents its own set of challenges. The availability of high-quality and reliable data is crucial for developing accurate ML models. A rigorous validation process and the integration of ML into clinical practice is essential for practical implementation. We furthermore highlight the potential risks associated with self-fulfilling prophecies and feedback loops, emphasizing the importance of transparency, interpretability, and trustworthiness in AI and ML models. These issues need to be addressed in order to establish reliable and trustworthy AI and ML models.

Conclusion

In this article, we overview concepts and examples of AI and ML research in the resuscitation field. Moving forward, appropriate understanding of ML and collaboration with relevant experts will be essential for researchers and clinicians to overcome the challenges and harness the full potential of AI and ML in resuscitation.

Diagnostic accuracy of pelvic imaging for acute pelvic inflammatory disease in an emergency care setting: a systematic review and meta-analysis

The aim of this review is to investigate the diagnostic accuracy or performance of contrast-enhanced computed tomography (CT) and magnetic resonance imaging (MRI) for acute pelvic inflammatory disease (PID) in an emergency care setting. We searched for studies on the diagnostic test accuracy of contrast-enhanced CT or MRI for women of reproductive age with acute abdominal pain using MEDLINE, Embase, Cochrane Central Register of Controlled Trials, International Clinical Trials Registry Platform, and ClinicalTrials.gov. The reference standard was gynecological examinations by gynecologists using standard diagnostic criteria with or without laparoscopy or transcervical endometrial biopsy. Two reviewers undertook screening of records, data extraction, and assessment of the risk of bias in each included study using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A bivariate model was used for the meta-analysis. Of 2,619 screened studies, three studies investigating contrast-enhanced CT and one study investigating MRI were eligible, including a total 635 patients and with a median prevalence of acute PID of 29%. All of the included studies had a high risk of bias for a reference standard and had some applicability concerns. Contrast-enhanced CT had a pooled sensitivity of 0.79 (95% confidence interval [CI], 0.52-0.93) and specificity of 0.99 (95% CI, 0.94-1.00). Magnetic resonance imaging had a sensitivity of 0.95 (95% CI, 0.76-1.00) and specificity of 0.89 (95% CI, 0.52-1.00). Contrast-enhanced CT might serve as a practical alternative to gynecological examination in the diagnosis of acute PID in an emergency care setting, however, the evidence was uncertain. The evidence on MRI was also very uncertain.