A validated outcome categorization of the knee society score for total knee arthroplasty
Bibliographic record
Abstract
PURPOSE: To determine cutoff values for the Knee Society Scores (KSS) indicative of a categorical scale of medium-term outcomes. METHODS: One hundred and fifty-five patients who underwent primary cruciate-retaining TKA with a patellar button for osteoarthritis at a single-centre were assessed prospectively by the KSS and short-form Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) simultaneously at the 3-year follow-up. A validated categorization of the WOMAC score was used as a standard. The area under the curve (AUC) of receiver-operating characteristic (ROC) was used to assess the discriminative analysis accuracy of the, and the Youden index estimated the optimal cutoff point. RESULTS: For the KSS-knee score, the cutoff for an excellent outcome was 90.3 (AUC 0.75, 95% CI 0.71-0.78), 76.6 (AUC 76.6, 95% CI 0.70-076) for good, 64.8 (AUC 0.76, 95% CI 0.72-0.79) for fair, and < 64.8 (AUC 0.69, 95% CI 0.67-0.73) for poor. For the KSS-function score, the cutoff values were 85.2 (AUC 0.71, 95% CI 0.69-0.75), 73.1 (AUC 0.72, 95% CI, 0.70-0.76), 55.7 (AUC 0.70, 95% CI 0.71-0.74), and < 55.7 (AUC 0.68, 95% CI 0.66-0.72), respectively. CONCLUSION: A KSS-knee score ≥ of 90 was considered an excellent outcome, 77 good, 65 fair, and < 65 poor. For the KSS-function, those values are 85, 73, 56 and < 56, respectively. The treatment outcome's judgement may be clearer for the surgeon concerning a particular patient when using cutoff values for the scoring system employed, such as those determined in the present study. LEVEL OF EVIDENCE: II.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".