Physicians' accuracy and interrator reliability for the diagnosis of unstable meniscal tears in patients having osteoarthritis of the knee.
Bibliographic record
Abstract
OBJECTIVE: To determine clinicians' accuracy and reliability for the clinical diagnosis of unstable meniscus tears in patients with symptomatic osteoarthritis of the knee. DESIGN: A prospective cohort study. SETTING: A single tertiary care centre. PATIENTS: One hundred and fifty-two patients with symptomatic osteoarthritis of the knee refractory to conservative medical treatment were selected for prospective evaluation of arthroscopic debridement. INTERVENTION: Arthroscopic debridement of the knee, including meniscal tear and chondral flap resection, without abrasion arthroplasty. OUTCOME MEASURES: A standardized assessment protocol was administered to each patient by 2 independent observers. Arthroscopic determination of unstable meniscal tears was recorded by 1 observer who reviewed a video recording and was blinded to preoperative data. Those variables that had the highest interobserver agreement and the strongest association with meniscal tear by univariate methods were entered into logistic regression to model the best prediction of resectable tears. RESULTS: There were 92 meniscal tears (77 medial, 15 lateral). Interobserver agreement between clinical fellows and treating surgeons was poor to fair (kappa < 0.4) for all clinical variables except radiographic measures, which were good. Fellows and surgeons predicted unstable meniscal tear preoperatively with equivalent accuracy of 60%. Logistic regression modelling revealed that a history of swelling and a ballottable effusion were negative predictors. A positive McMurray test was the only positive predictor of unstable meniscal tear. "Mechanical" symptoms were not reliable predictors in this prospective study. The model was 69% accurate for all patients and 76% for those with advanced medial compartment osteoarthritis defined by a joint space height of 2 mm or less. CONCLUSIONS: This study underscored the difficulty in using clinical variables to predict unstable medial meniscal tears in patients with pre-existing osteoarthritis of the knee. The lack of interobserver agreement must be overcome to ensure that the findings can be generalized to other physician observers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".