Accuracy and reliability of arthroscopic estimates of cartilage lesion size in a plastic knee simulation model
Bibliographic record
Abstract
PURPOSE: The goal of the study was to determine the accuracy and reliability of arthroscopic percent area estimates in a plastic knee simulation model. A second goal was to determine the effect of lesion location within the knee and lesion size on accuracy and reliability. TYPE OF STUDY: Cross-sectional study of arthroscopic estimates of cartilage lesion size. METHODS: Three experienced arthroscopists performed 3 sets arthroscopic percent area estimates in 5 different plastic knees. Each knee had lesions drawn on 5 surfaces (patellar, medial and lateral femoral condyle, medial and lateral tibial plateaus). Accuracy and reliability were studied using Bland and Altman limits of agreement (LOA) and intraclass correlation coefficients. RESULTS: There was a strong tendency to overestimate lesion size by over 100% on the femoral and patellar surfaces. Intraobserver and interobserver reliabilities were generally poor. The range for the 95% LOA (+/- 1.96 standard deviation [SD] of the difference scores) between repeated measurements was almost 6 times the size of the lesion itself. Reliability of estimates was poorest for the largest lesions and worse at femoral, lateral tibial, and patellar sites. CONCLUSIONS: Assessments of arthroscopic measurements using LOA found that accuracy and reliability were generally poor, although results were better at the medial tibial plateau and for smaller lesions. In spite of these problems, arthroscopy remains a promising measurement tool because it permits physical assessment of cartilage integrity. This study sets the foundations for improvement in techniques of arthroscopic measurement of cartilage lesion size.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".