National Institute of Health and Care Excellence Clinical Criteria for the Diagnosis of Knee Osteoarthritis: A Prospective Diagnostic Accuracy Study in Individuals With Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: The National Institute of Health and Care Excellence (NICE) criteria for osteoarthritis (OA) obviate the need for physical examination or imaging, and their use may improve timely diagnosis of OA. However, they have not been validated. METHODS: Within a larger study of individuals with type 2 diabetes, participants with and without self-reported knee pain underwent assessment of the NICE criteria for knee OA by questionnaire (index test) and clinical evaluation for established or possible knee OA by a rheumatologist (reference standard). We calculated the sensitivity, specificity, likelihood ratio positive (LR+), and likelihood ratio negative (LR-) of the NICE criteria and modified NICE criteria without the stiffness criterion. RESULTS: Our study included 96 participants: the mean ± SD age was 65.4 ± 8.3 years and 52% were women. Individuals who fulfilled the NICE criteria for knee OA (55.2%) included a spectrum of pain severity on an 11-point pain numeric rating scale with a median score of 5 (range 1-9). Rheumatologist assessment identified 56 participants (58.3%) with symptomatic knee OA. The sensitivity, specificity, LR+, and LR- of the NICE criteria for symptomatic knee OA were 0.84 (95% confidence interval [CI] 0.74-0.94), 0.85 (95% CI 0.74-0.96), 5.6, and 0.19, respectively. For the modified NICE criteria, these were 0.89 (95% CI 0.82-0.97), 0.85 (95% CI 0.74-0.96), 5.93, and 0.13. CONCLUSION: The NICE criteria have high sensitivity and specificity for detecting symptomatic knee OA in a population with type 2 diabetes. We found that a modified version, omitting the stiffness criterion, performed similarly. These criteria should be validated in other settings and populations.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".