Reliability and Validity of the Arthroscopic International Cartilage Repair Society Classification System: Correlation With Histological Assessment of Depth
Bibliographic record
Abstract
PURPOSE: To determine the interobserver reliability of the International Cartilage Repair Society (ICRS) grading system of chondral lesions in cadavers, to determine the intraobserver reliability of the ICRS grading system comparing arthroscopy and video assessment, and to compare the arthroscopic ICRS grading system with histological grading of lesion depth. METHODS: Eighteen lesions in 5 cadaveric knee specimens were arthroscopically graded by 7 fellowship-trained arthroscopic surgeons using the ICRS classification system. The arthroscopic video of each lesion was sent to the surgeons 6 weeks later for repeat grading and determination of intraobserver reliability. Lesions were biopsied, and the depth of the cartilage lesion was assessed. Reliability was calculated using intraclass correlations. RESULTS: The interobserver reliability was 0.67 (95% confidence interval, 0.5-0.89) for the arthroscopic grading, and the intraobserver reliability with the video grading was 0.8 (95% confidence interval, 0.67-0.9). A high correlation was seen between the arthroscopic grading of depth and the histological grading of depth (0.91); on average, surgeons graded lesions using arthroscopy a mean of 0.37 (range, 0-0.86) deeper than the histological grade. CONCLUSIONS: The arthroscopic ICRS classification system has good interobserver and intraobserver reliability. A high correlation with histological assessment of depth provides evidence of validity for this classification system. CLINICAL RELEVANCE: As cartilage lesions are treated on the basis of the arthroscopic ICRS classification, it is important to ascertain the reliability and validity of this method.
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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 | 0.014 | 0.044 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".