382 Maximising the relevance and dissemination of the IOC medical consensus statements: what are the consensus statements and how are they used in literature?
Bibliographic record
Abstract
Background The International Olympic Committee (IOC) Medical and Scientific Commission has a goal to provide guidance on athlete health for sports organisations. One strategy to meet this goal has been the development and publication of sports medicine consensus statements. It is currently unknown if there has been use of the consensus statements or if the overall goal of the statements – to improve athlete health and wellbeing - has been achieved. Objective To identify and summarise citation measures of the IOC medical consensus statements. Design Citation analysis. Methods IOC medical consensus statements published from 2004 to 2018, and citing publications, were sourced from the IOC website, Scopus database and Google Scholar. Descriptive analyses over time of the number of consensus statements and citing documents with summaries of the authorship countries and keywords. Citation analyses were conducted to model links between consensus statements and citing publications, field weighted citation index (FWCI), and the SCImago Journal Ranking. Results Twenty-seven consensus statements linked to the IOC medical and scientific commission were identified, addressing a range of topics from broad health and social issues to specific clinical topics. Authors from 30 countries contributed to the statements while citing papers were authored from 86 countries. Concussion was the most prominent key term in all citing documents. The youth athletic development statement has the highest FWCI (19.6), followed by concussion(18.8); load(12.3); relative energy deficiency(11.3); platelet-rich plasma(10.1); and supplements(9.9). Conclusions Several consensus statements are widely used and cited in the literature while others have been less impactful through citation measures. The countries that use and cite consensus statements are much more diverse globally than those that author them. Consideration of how the statements are used in practice and outside of the academic literature needs to be explored.
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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 | 0.541 | 0.847 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.055 | 0.037 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.026 | 0.020 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".