Consensus meetings and statements are flawed by design: A narrative review with suggestions for improvements
Bibliographic record
Abstract
Consensus statements from the sport and exercise medicine community are now fairly common. More recently, the statements appear more prescriptive, strongly recommending particular approaches to research or treatment. The most recent statement on methods for reporting sport injury surveillance studies included an extension to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) 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. The challenge is that by definition, consensus is not unanimity. Therefore, consensus recommendations are sometimes considered flawed at a later date. This is expected if we gain new knowledge. However, the consensus methods themselves may also inadvertently lead to a suppression of contrary but valid opinions. The purpose of this narrative review it to propose a different model for consensus meetings and statements that embraces dissenting opinions, leading to increased transparency. In brief, the method is based on how Supreme Courts functions, allowing 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 between 2005 and 2020. Such an approach will help ensure that clinicians, researchers and journals are not inappropriately influenced by recommendations from consensus statements, where experts continue to have important disagreements about the strength and interpretation of the evidence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.645 | 0.814 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.025 | 0.064 |
| Open science | 0.011 | 0.015 |
| Research integrity | 0.021 | 0.022 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".