Meta-research on pragmatism of randomized trials: rationale and design of the PragMeta database

Benchmarking Human-AI collaboration for common evidence appraisal tools

BACKGROUND AND OBJECTIVE

It is unknown whether large language models (LLMs) may facilitate time- and resource-intensive text-related processes in evidence appraisal. The objective was to quantify the agreement of LLMs with human consensus in appraisal of scientific reporting (Preferred Reporting Items for Systematic reviews and Meta-Analyses [PRISMA]) and methodological rigor (A MeaSurement Tool to Assess systematic Reviews [AMSTAR]) of systematic reviews and design of clinical trials (PRagmatic Explanatory Continuum Indicator Summary 2 [PRECIS-2]) and to identify areas where collaboration between humans and artificial intelligence (AI) would outperform the traditional consensus process of human raters in efficiency.

STUDY DESIGN AND SETTING

Five LLMs (Claude-3-Opus, Claude-2, GPT-4, GPT-3.5, Mixtral-8x22B) assessed 112 systematic reviews applying the PRISMA and AMSTAR criteria and 56 randomized controlled trials applying PRECIS-2. We quantified the agreement between human consensus and (1) individual human raters; (2) individual LLMs; (3) combined LLMs approach; (4) human-AI collaboration. Ratings were marked as deferred (undecided) in case of inconsistency between combined LLMs or between the human rater and the LLM.

RESULTS

Individual human rater accuracy was 89% for PRISMA and AMSTAR, and 75% for PRECIS-2. Individual LLM accuracy was ranging from 63% (GPT-3.5) to 70% (Claude-3-Opus) for PRISMA, 53% (GPT-3.5) to 74% (Claude-3-Opus) for AMSTAR, and 38% (GPT-4) to 55% (GPT-3.5) for PRECIS-2. Combined LLM ratings led to accuracies of 75%-88% for PRISMA (4%-74% deferred), 74%-89% for AMSTAR (6%-84% deferred), and 64%-79% for PRECIS-2 (29%-88% deferred). Human-AI collaboration resulted in the best accuracies from 89% to 96% for PRISMA (25/35% deferred), 91%-95% for AMSTAR (27/30% deferred), and 80%-86% for PRECIS-2 (76/71% deferred).

CONCLUSION

Current LLMs alone appraised evidence worse than humans. Human-AI collaboration may reduce workload for the second human rater for the assessment of reporting (PRISMA) and methodological rigor (AMSTAR) but not for complex tasks such as PRECIS-2.

Methods for pragmatic randomized clinical trials of pain therapies: IMMPACT statement

ABSTRACT

Pragmatic, randomized, controlled trials hold the potential to directly inform clinical decision making and health policy regarding the treatment of people experiencing pain. Pragmatic trials are designed to replicate or are embedded within routine clinical care and are increasingly valued to bridge the gap between trial research and clinical practice, especially in multidimensional conditions, such as pain and in nonpharmacological intervention research. To maximize the potential of pragmatic trials in pain research, the careful consideration of each methodological decision is required. Trials aligned with routine practice pose several challenges, such as determining and enrolling appropriate study participants, deciding on the appropriate level of flexibility in treatment delivery, integrating information on concomitant treatments and adherence, and choosing comparator conditions and outcome measures. Ensuring data quality in real-world clinical settings is another challenging goal. Furthermore, current trials in the field would benefit from analysis methods that allow for a differentiated understanding of effects across patient subgroups and improved reporting of methods and context, which is required to assess the generalizability of findings. At the same time, a range of novel methodological approaches provide opportunities for enhanced efficiency and relevance of pragmatic trials to stakeholders and clinical decision making. In this study, best-practice considerations for these and other concerns in pragmatic trials of pain treatments are offered and a number of promising solutions discussed. The basis of these recommendations was an Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) meeting organized by the Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks.

Use of pragmatic randomized trials in multiple sclerosis: A systematic overview

BACKGROUND

Pragmatic trials are increasingly recognized for providing real-world evidence on treatment choices.

OBJECTIVE

The objective of this study is to investigate the use and characteristics of pragmatic trials in multiple sclerosis (MS).

METHODS

Systematic literature search and analysis of pragmatic trials on any intervention published up to 2022. The assessment of pragmatism with PRECIS-2 (PRagmatic Explanatory Continuum Indicator Summary-2) is performed.

RESULTS

We identified 48 pragmatic trials published 1967-2022 that included a median of 82 participants (interquartile range (IQR) = 42-160) to assess typically supportive care interventions (n = 41; 85%). Only seven trials assessed drugs (15%). Only three trials (6%) included >500 participants. Trials were mostly from the United Kingdom (n = 18; 38%), Italy (n = 6; 13%), the United States and Denmark (each n = 5; 10%). Primary outcomes were diverse, for example, quality-of-life, physical functioning, or disease activity. Only 1 trial (2%) used routinely collected data for outcome ascertainment. No trial was very pragmatic in all design aspects, but 14 trials (29%) were widely pragmatic (i.e. PRECIS-2 score ⩾ 4/5 in all domains).

CONCLUSION

Only few and mostly small pragmatic trials exist in MS which rarely assess drugs. Despite the widely available routine data infrastructures, very few trials utilize them. There is an urgent need to leverage the potential of this pioneering study design to provide useful randomized real-world evidence.