Consensus methods in patellofemoral pain: how rigorous are they? A scoping review
Bibliographic record
Abstract
OBJECTIVE: Clinicians treating patients with patellofemoral pain (PFP) rely on consensus statements to make the best practice recommendations in the absence of definitive evidence on how to manage PFP. However, the methods used to generate and assess agreement for these recommendations have not been examined. Our objective was to map the methods used to generate consensus-based recommendations for PFP and apply four novel questions to assess the rigour of consensus development. DESIGN: Scoping review. DATA SOURCES: We searched Medline, SPORTDiscus, CINAHL and Embase from inception to May 2022 to identify consensus-derived statements or practice guidelines on PFP. The Joanna Briggs Institute Manual for Evidence Synthesis was followed to map the existing evidence. We measured the consensus methods based on four sets of questions addressing the panel composition, application of the consensus method chosen, agreement process and the use of evidence mapping. ELIGIBILITY CRITERIA: All consensus statements or clinical guidelines on PFP were considered. RESULTS: Twenty-two PFP consensus statements were identified. Panel composition: 3 of the 22 (14%) consensus groups reported the panellists' experience, 2 (9%) defined a desired level of expertise, 10 (45%) reported panellist sex and only 2 (9%) included a patient. Consensus method: 7 of 22 (32%) reported using an established method of consensus measurement/development. Agreement process: 10 of 22 (45%) reported their consensus threshold and 2 (9%) acknowledged dissenting opinions among the panel. Evidence mapping: 6 of 22 (27%) reported using systematic methods to identify relevant evidence gaps. CONCLUSIONS: PFP consensus panels have lacked diversity and excluded key partners including patients. Consensus statements on PFP frequently fail to use recognised consensus methods, rarely describe how 'agreement' was defined or measured and often neglect to use systematic methods to identify evidence gaps.
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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 | 0.574 | 0.834 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.016 | 0.011 |
| Bibliometrics | 0.039 | 0.040 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.030 | 0.037 |
| Open science | 0.011 | 0.015 |
| Research integrity | 0.014 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".