A Validation Study of the New Zealand Score for Hip and Knee Surgery
Bibliographic record
Abstract
UNLABELLED: In the absence of consensus over criteria for performing total knee arthroplasty, the variability of symptom burden, and limited resources, some ways to prioritize whether and when to treat would be useful. In the UK, some payers use the New Zealand score to determine access to an orthopaedic surgeon despite limited validation. We tested convergent validity of this score and ascertained its ability to discriminate between groups of patients with high or low disease burden as determined by a validated disease-specific measure. The sample included patients being considered for total knee arthroplasty at one hospital. Convergent validity was tested against the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). The ability of the New Zealand score to discriminate between high and low disease burdens was tested by plotting a receiver operating characteristic curve. Correlations between the New Zealand score and WOMAC pain and function were moderate (0.5 and 0.54, respectively). The area under the receiver operating characteristic curve was 0.77, suggesting the New Zealand score was able to discriminate. This study supports the validity of the New Zealand score. However, additional multisite and extended evaluations are needed before we would recommend widespread implementation. LEVEL OF EVIDENCE: Level I, economic and decision analyses. See the Guidelines for Authors for a complete description of levels of evidence.
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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 | 0.022 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".