Correlation between Trochlear Dysplasia and the Notch Index
Bibliographic record
Abstract
PURPOSE: To evaluate the correlation between trochlear dysplasia and the notch index. METHODS: Magnetic resonance images (MRI) of 95 knees in 54 male and 36 female patients aged 4 to 74 (mean, 28) years were reviewed by 2 musculoskeletal radiologists. Standard MRI sequences were used. Based on the Dejour classification of trochlear dysplasia, the knees were classified into normal or types A, B, C, and D. A notch index of <0.2 was considered narrow. Normal knees and knees with trochlear dysplasia were compared. RESULTS: 60 of the 95 knees had trochlear dysplasia, of which 39 were Dejour type A, 13 were type B, 7 were type C, and one was type D. Dejour types B, C, and D were combined as non-type A. Inter-observer agreement in assessing the notch index was good (Kappa=0.6). The mean notch indices in normal knees and knees with trochlear dysplasia were comparable (0.161 vs. 0.157, p=0.18), as were in Dejour type A and non-type A knees (0.154 vs. 0.160, p=0.54) and in Dejour types A, B, C, and D knees (0.154 vs. 0.165 vs 0.153 vs. 0.2, p=0.17-0.7). The rate of ACL injuries was similar in patients with normal knees and those with type-A trochlear dysplasia. A low notch index (narrow notch) was not associated with ACL injury. CONCLUSION: The notch index and trochlear morphology are 2 independent entities. A narrow notch does not imply a shallow trochlear grove.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".