The importance of sports medicine for the Vancouver Olympic Games
Bibliographic record
Abstract
I remember Turin well. As the head doctor for Norway I witnessed our best alpine skier injure his knee at the very first vertical in the downhill race. The injury probably prevented him from winning a medal that day. However, he managed to recover incredibly fast and won the Gold medal in Super-G 1 week later. Many have not been as lucky as this! In the current edition of IPHP , Florenes et al describe the injury incidence among World Cup alpine skiers, and their injury rate is indeed high!1 (see page 973) This is the first large cohort study to examine the overall injury risk and detailed injury patterns among World Cup alpine skiers during the competitive season. The main findings reveal that the injury incidence for elite alpine skiers was higher than reported previously, in general higher for men than women, and that the injury rate increased with skiing speed. The knee was the most commonly injured body part, with a majority of severe injuries. Notably, as many as 38% of all time-loss injuries caused an absence of more than 1 month (more than 28 days). …
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.067 | 0.022 |
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".