Diagnosing Meniscal Pathology and Understanding How to Evaluate a Postoperative Meniscus Based on the Operative Procedure
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) represents the preferred noninvasive imaging technique to diagnose meniscal pathology in the pre- and postoperative setting. Furthermore, characterization of meniscal tissue MR properties has been possible by the development of advanced MRI techniques. Suspected meniscal tears are a frequent indication for MRI and the International Society of Arthroscopy, Knee Surgery and Orthopaedic Sports Medicine (ISAKOS) classification system has been developed to facilitate accurate and uniform reporting of such meniscal tears. Partial meniscectomy and meniscal suture repair are among the commonly performed procedures and several signs have been described to detect postoperative recurrent tears on MRI. Other techniques that have proven useful for meniscal assessment are ultrasound (US) and computed tomography (CT) arthrography. In recent years, US is being increasingly used in the selective assessment of some meniscal pathology such as tears, parameniscal cysts and meniscal extrusion as it is a relatively inexpensive, accessible, and safe technique. CT arthrography has been advocated as an acceptable alternative in patients with contraindications for MRI, with comparable diagnostic performance.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".