Qualitative Magnetic Resonance Imaging Assessment of the Semimembranosus Tendon in Patients with Medial Meniscal Tears
Bibliographic record
Abstract
Background: To determine if there is an association between semimembranosus tendinosis and medial meniscal tears using MRI. Methods: A retrospective review of knee 3T MRI scans was performed to determine the presence or absence of medial meniscal tears in patients with semimembranosus tendinosis. All studies were interpreted by two musculoskeletal radiologists. Univariate association for the presence of semimembranosus tendinosis and medial meniscal tears was performed with a Chi-square test followed by logistic regression modelling among statistically significant associations. Results: A total of 150 knee MRI scans were reviewed (age 32.8 ± 7.1 years; 70 females). Semimembranosus tendinosis was present in 66 knees (44%) in the patient population. Semimembranosus tendinosis was present in 81% of patients with meniscal tears versus 36% of patients without meniscal tears (p < 0.0001). This association remained statistically significant when adjusted for age and sex with an adjusted odds ratio of 7.0 (p < 0.0003). Models adjusted for the above covariates and containing the interaction term produced an adjusted odds ratio of 13.0 (p < 0.0001) in men, while in women this association was non-significant with an adjusted odds ratio of 2.0 (p = 0.42). Conclusions: Subjects with semimembranosus tendinosis were seven times more likely to have medial meniscal tears even when adjusting for sex and age. This could help guide the appropriate postmeniscal repair rehabilitation protocol.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".