Amsterdam 2022 process: A summary of the methodology for the Amsterdam International Consensus on Concussion in Sport
Bibliographic record
Abstract
The purpose of this paper is to summarise the consensus methodology that was used to inform the International Consensus Statement on Concussion in Sport (Amsterdam 2022). Building on a Delphi process to inform the questions and outcomes from the 5th International Conference on Concussion in Sport, the Scientific Committee identified key questions, the answers to which would help encapsulate the current science in sport-related concussion and help guide clinical practice. Over 3½ years, delayed by 2 years due to the pandemic, author groups conducted systematic reviews on each selected topic. The 6th International Conference on Concussion in Sport was held in Amsterdam (27-30 October 2022) and consisted of 2 days of systematic review presentations, panel discussions, question and answer engagement with the 600 attendees, and abstract presentations. This was followed by a closed third day of consensus deliberations by an expert panel of 29 with observers in attendance. The fourth day, also closed, was dedicated to a workshop to discuss and refine the sports concussion tools (Concussion Recognition Tool 6 (CRT6), Sport Concussion Assessment Tool 6 (SCAT6), Child SCAT6, Sport Concussion Office Assessment Tool 6 (SCOAT6) and Child SCOAT6). We include a summary of recommendations for methodological improvements for future research that grew out of the systematic reviews.
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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 | 0.312 | 0.334 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.039 | 0.015 |
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".