The road to consensus: lessons learned from FAIR and recommendations for future consensus activities
Bibliographic record
Abstract
The consensus process, applied frequently in sport and exercise medicine (SEM), offers a structured framework to generate high-quality practical recommendations and/or identify research priorities to promote athlete health when robust clinical trial evidence is limited.Consensus activities integrate the best available evidence with the collective knowledge, experience and expertise of researchers and sport partners, including athletes, coaches, parents/carers, health and exercise practitioners, sport science and performance professionals, sport administrators and policy makers (also referred to as the athlete entourage, interest holders, knowledge users and consumers).To ensure transparency, repeatability and relevance, the ACCORD (accurate consensus reporting document) guidelines outline how to conduct and communicate consensus activities, and the British Journal of Sports Medicine also provides author guidelines for consensus statements. 1 Between April 2023 and September 2025, we conducted the female, woman and/or girl athlete injury prevention (FAIR) consensus, undertaking five systematic reviews, 2-6 a scoping review 7 and a concept mapping exercise.8 These activities informed the recommendations, which were discussed and voted on in a two-stage process, with 56 actionable final recommendations submitted for publication.9 While the FAIR consensus conduct and reporting aligned with the ACCORD guidelines, this editorial aims to summarise lessons learnt and recommendations to improve future consensus processes.1 LESSONS LEARNED AND SUGGESTIONS ON 'HOW TO' TO NAVIGATE THE ROAD TO CONSENSUSUndertaking a consensus activity the size of FAIR took about 2 years and approximately 100 volunteers.A smaller consensus activity may be quicker and require fewer resources.Regardless, there are critical considerations that can enhance the consensus process, which build on the ACCORD guidance table 1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.481 | 0.624 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.020 | 0.029 |
| Scholarly communication | 0.039 | 0.059 |
| Open science | 0.016 | 0.032 |
| Research integrity | 0.027 | 0.038 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".