Reaching consensus within patellofemoral pain research: Protocol for a scoping review
Bibliographic record
Abstract
Consensus is an often neglected but important part of the scientific process. Consensus agreement allows researchers to agree on fundamentals such as terminology and taxonomy, to establish core outcome sets for reporting on medical conditions, and to set research priorities. Consensus methods are invoked by the scientific community to provide answers on topics with little to no previous research, or to provide further guidance when the available evidence is unclear. In the medical literature the most common methods for assessing consensus are the Delphi, RAND-UCLA and Nominal Group Technique. However, consensus methods and their subsequent published 'consensus statements' often: exclude relevant stakeholders or fail to justify their expert panel selection; neglect to use systematic/scoping reviews to inform what questions or recommendations their consensus panel will rank or vote upon; and often omit the levels of agreement amongst panel members, or worse suppress the existance of important minority views. Given their importance in providing direction to the scientific community, methods of consensus development and their subsequent reporting require further scrutiny. We propose to use the methods of consensus development within the patellofemoral pain field to review: how consensus has been generated; who has been invited/involved; whether formal literature reviews have supported statements and recommendations; and how subsequent agreements/dissent has been reported. By studying the subset of statements on patellofemoral pain we hope to inform future recommendations on the production of rigorous consensus development and reporting.
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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 | 0.162 | 0.196 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.092 | 0.029 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".