Can the Oxford Knee and Hip Score identify patients who do not require total knee or hip arthroplasty?
Bibliographic record
Abstract
Aims The aim of this study was to determine if the Oxford Knee and Hip Score (OKHS) can accurately predict when a primary knee or hip referral is deemed nonsurgical versus surgical by the surgeon during their first consultation, and to identify nonsurgical OKHS screening thresholds. Patients and Methods We retrospectively reviewed pre-consultation OKHS for all consecutive primary total knee arthroplasty (TKA) and total hip arthroplasty (THA) consultations of a single surgeon over three years. The 1436 knees (1016 patients) and 478 hips (388 patients) included were categorized based on the surgeon’s decision into those offered surgery during the first consultation versus those not (nonsurgical). Spearman’s rank correlation coefficients and receiver operating characteristic (ROC) curve analysis were performed. Results Oxford Scores were better for the nonsurgical cohorts (p < 0.001) and correlated with the surgical decision (p < 0.001). ROC area under the curve values for knees (0.83, 95% confidence intervals (CI) 0.81 to 0.85) and hips (0.87, 95% CI 0.84 to 0.91) were excellent. A conservative and effective threshold for knees is Oxford Knee Score (OKS) > 32 points (sensitivity = 0.997, negative predictive value (NPV) = 0.992) and for hips is Oxford Hip Score (OHS) > 34 points (sensitivity = 0.997, NPV = 0.978). Severable potential lower OKHS thresholds were identified. Conclusion Pre-consultation OKHS demonstrate good ability to predict when a primary TKA or THA referral will be deemed nonsurgical in a single surgeon’s practice. Multiple OKHS thresholds can effectively screen out nonsurgical referrals. Cite this article: Bone Joint J 2019;101-B(6 Supple B):23–30.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".