Anterior Cruciate Ligament Rupture
Bibliographic record
Abstract
The number of anterior cruciate ligament (ACL) injuries among young athletes has increased over the past 2 decades. It is currently estimated that 47 per 100,000 boys aged 10 to 19 years will require surgery for an ACL injury each year.12 Apart from the immediate debilitating effects, there are serious long-term consequences of ACL injuries, including chronic knee instability, cartilage damage, and osteoarthritis—all leading to decreased activity levels. On average, 50% of individuals will develop radiographic signs of osteoarthritis, which is associated with pain and functional impairment, 10 to 20 years after diagnosis.14 Thus, it is imperative to identify young athletes that may be susceptible to ACL injuries and implement preventative training measures in these individuals. The incidence rate of ACL injuries among young, male, American football players has been reported to be 0.58 ACL injuries per 10,000 athlete-exposures, with an athlete-exposure being defined as 1 athlete participating in either 1 game or 1 practice.23
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".