Consensus statements that fail to recognise dissent are flawed by design: a narrative review with 10 suggested improvements
Bibliographic record
Abstract
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.
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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 | 0.376 | 0.616 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.014 |
| Bibliometrics | 0.019 | 0.010 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.015 | 0.030 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.016 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".