Classifiers of anterior cruciate ligament status in female and male adolescents using return‐to‐activity criteria
Bibliographic record
Abstract
Abstract Purpose A lack of standardization exists for functional tasks in return‐to‐activity (RTA) guidelines for adolescents with anterior cruciate ligament injury (ACLi). Identifying the variables that discern ACLi status among adolescents is a first step in the creation of such guidelines following surgical reconstruction. This study investigated the use of classification models to discern ACLi status of adolescents with and without injury using spatiotemporal variables from functional tasks typically used in RTA guidelines for adults. Methods Sixty‐four adolescents with ACLi and 70 uninjured adolescents completed single‐limb hops, lunges, squats, countermovement jumps and drop‐vertical jumps. Jumping distances, heights, and depths were collected. Decision trees (DTs) were used to classify ACLi status and were evaluated using the F‐measure (F1), kappa statistic (ĸ) and area under the precision–recall curve (PRC). Independent t tests and effect sizes were calculated for each important classifier of the DT models. Results A five‐variable model classified ACLi status with an accuracy of 67.5% (F1 = 0.6842; ĸ = 0.350; PRC = 0.491) with sex as a classifier. Significant differences were found in three of the four spatiotemporal variables (p ≤ 0.002). Separate models then classified ACLi status in males and females with an accuracy of 53.3% (F1 = 0.5882; ĸ = 0.0541; PRC = 0.476) and 76.9% (F1 = 0.7692; ĸ = 0.541; PRC = 0.528), respectively, with significant differences for all variables (p ≤ 0.013). Conclusions Among the DT models, females were better able to classify ACLi status compared to males, highlighting the importance of sex‐specific rehabilitation guidelines for adolescents. Level of Evidence Level III.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".