Diagnostic criteria for early hip osteoarthritis: first steps, based on the CHECK study
Bibliographic record
Abstract
OBJECTIVES: Although there is a general focus on early diagnosis and treatment of hip OA, there are no validated diagnostic criteria for early-stage hip OA. The current study aimed to take the first steps in developing diagnostic criteria for early-stage hip OA, using factors obtained through history taking, physical examination, radiography and blood testing at the first consultation in individuals presenting with hip pain, suspicious for hip OA, in primary care. METHODS: Data of the 543 individuals with 735 symptomatic hips at baseline who had any follow-up data available from the prospective CHECK cohort study were used. A group of 26 clinical experts [general practitioners (GPs), rheumatologists and orthopaedic surgeons] evaluated standardized clinical assessment forms of all subjects on the presence of clinically relevant hip OA 5-10 years after baseline. Using the expert-based diagnoses as reference standard, a backward selection method was used to create predictive models based on pre-defined baseline factors from history taking, physical examination, radiography and blood testing. RESULTS: Prevalence of clinically relevant hip OA during follow-up was 22%. Created models contained four to eight baseline factors (mainly WOMAC pain items, painful/restricted movements and radiographic features) and obtained area under the curve between 0.62 (0.002) and 0.71 (0.002). CONCLUSION: Based on clinical and radiographic features of hip OA obtained at first consultation at a GP for pain/stiffness of the hip, the prediction of clinically relevant hip OA within 5-10 years was 'poor' to 'fair'.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".