Three‐Dimensional Intercondylar Notch Volumes in a Skeletally Immature Pediatric Population: A Magnetic Resonance Imaging–Based Anatomic Comparison of Knees With Torn and Intact Anterior Cruciate Ligaments
Bibliographic record
Abstract
PURPOSE: To determine whether 3-dimensional notch volume, measured with magnetic resonance imaging (MRI), differs significantly between knees with torn and intact anterior cruciate ligaments (ACLs) after sports injury in a skeletally immature pediatric population. METHODS: MRI studies of 50 pediatric patients (age range, 10 to 17 years) with ACL tears were compared with 50 age- and sex-matched intact-ACL control patients. All patients had open physes and underwent MRI after a sports injury. Notch volume was calculated through manual segmentation of notch boundaries seen on axial 1.5-T proton density-weighted images. Two-dimensional (2D) measurements (notch width and notch width index) were made on coronal proton density-weighted MRI studies. Notch volume was compared between groups by use of the Mann-Whitney U test. Pearson correlation coefficients were also calculated between indices. RESULTS: Notch volume was significantly lower in knees with ACL tears than in control knees (5.5 ± 1.1 cm(3)v 6.4 ± 1.5 cm(3), P = .002), whereas 2D notch width and notch width index did not differ significantly between these groups. Girls had significantly smaller notch volumes than boys (5.4 ± 1.2 cm(3)v 6.5 ± 1.3 cm(3), P < .001). Notch volume was not correlated with age but was moderately correlated with 2D notch width (r = 0.485, P < .001). CONCLUSIONS: In adolescent patients with sports injuries, the 3-dimensional notch volume was significantly smaller in knees with ACL tears than in intact-ACL control knees. Notch volume was also significantly smaller in girls than in boys and did not vary significantly with age. LEVEL OF EVIDENCE: Level III, case-control study.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".