Comparing and Assessing Clinical Practice Guidelines and Consensus Statements for Healthy Athletes: A Scoping Review Protocol
Bibliographic record
Abstract
ABSTRACT Introduction Healthy athletes have unique physiological, psychological, and interpersonal needs that necessitate individualized prevention and management strategies. As such, this comprehensive protocol has been created to illustrate the scope, quality and coverage of these clinical practice guidelines (CPGs) and consensus statements among this specific population. Methods and Analysis Searches will be conducted in PubMed, Scopus, SPORTDiscus, Web of Science, EMBASE, Google Scholar and any grey literature within official international organizations. Eligible publications will include CPGs and consensus statements that are aimed towards healthy athletes, published in English or Spanish since 2000. The AGREE II instrument will be applied to assess the quality of the guidelines by two independent reviewers. Disagreements will be resolved by a third reviewer. We will use descriptive statistics to summarize the main characteristics of the guidelines. Discussion This scoping review seeks towards providing an in‐depth analysis on the mapping, explaining, and evaluating of the available CPGs and consensus statements of the population of healthy athletes. Moreover, it will identify thematic trends, methodological strengths, and potential evidence gaps that currently exist. Fundamentally, the results will provide valuable information towards development of guidelines, standardization efforts, and evidence‐based practice in the future within the field of sports medicine.
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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 | 0.275 | 0.287 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.025 | 0.020 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.035 | 0.008 |
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".