Bibliographic record
Abstract
Warm upWhile fi nishing our paper on injuries and illnesses during the Olympic Games in Vancouver, we were contemplating our achievements in research on protection of the athlete's health.Granted, 15 years ago, very little had been done except for the occasional epidemiological study, oftentimes with defi cient methodology.National and international sports federations were reluctant to even mention injury problems in their sport and not willing to allocate research money.How much this fi eld has changed over the years!The fi nal proof of the merit of this fi eld came with the International Olympic Committee (IOC) President Jacques Rogge's editorial in the British Journal of Sports Medicine 2009, in which he highlighted the new IOC initiatives in the protection of athletes.1 The most recent news comes from the American Orthopedic Society for Sports Medicine (AOSSM) and their Stop Sports Injuries campaign.2 Their background is the following: injury rates are rising.In a recent report from the Center for Disease Control, high school athletes accounted for an estimated 2 million injuries.More than 5 million sports-related injuries requiring medical treatment occur in children under 18 years old; 50% of the injuries are dueWe are getting there!
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 | 0.005 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.023 |
| Insufficient payload (model declined to judge) | 0.105 | 0.075 |
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".