The old knee in the young athlete: knowns and unknowns in the return to play conversation
Bibliographic record
Abstract
Twenty-year-old Sarah is a sport-obsessed amateur football player. She enjoys the social side of playing in a team sport, but most of all she loves the physical and mental challenge of game day. There is no better feeling than winning. She works hard on her skills and fitness, training twice a week with the team, and most other days on the cycle commute to the University and with her own gym programme. Maybe it was her young age1 and dodgy biomechanics2 that conspired against her. Perhaps it was the type of grass on her home pitch.3 But she winds up at your clinic clutching an MRI report that bears the dreaded diagnosis—she's ruptured her ACL. Return to play is a fundamental concern for athletes and sports medicine clinicians—one important benchmark for judging treatment success. For many athletes with ACL injury, their most pressing concerns are about meeting their own expectations, or those of significant others (eg, coach, team mates, family), for returning to their pre-injury level or sports performance. Sarah will be faced with three options when she is ready to return to play—return to the pre-injury level sport, change sports participation (either change sport or change level) or retire. Fact 1 : Excellent physical function on impairment-based and activity-based …
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.012 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".