Why all the fuss about paediatric ACL rupture: isn’t the meniscus much more important?
Bibliographic record
Abstract
When Ava, a 13-year-old basketball player, tried to change direction quickly to drive past her opponent, her knee buckled and she developed an acute haemarthrosis. Ava saw a general practitioner and a physiotherapist who advised her to ice her knee and regain her motion. She initially improved, obtained an over-the-counter brace and returned to playing basketball. The full extent of her injury was not recognised. Ava had weekly episodes of knee giving way when playing basketball and stopped playing her favourite sport. She continued to have a feeling of instability, swelling and pain with her daily activities. Four months after her injury, MRI of Ava’s knee confirmed the ACL rupture and a lateral meniscal tear. By the time she had a surgical appointment and an arthroscopy her lateral meniscus was almost absent; nothing separated the lateral femoral condyle and the adjacent tibial plateau (figure 1). We will never know the extent of the original injury to Ava’s lateral meniscus. However, we might suspect that the lack of early and specific recognition of the problem and the subsequent recurrent giving way episodes aggravated meniscal damage. Figure 1 Lateral compartment of the knee with only a small …
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.016 | 0.025 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".