Towards developing diagnostic criteria for early knee osteoarthritis: data from the CHECK study
Bibliographic record
Abstract
OBJECTIVES: There is a general consensus that a shift in focus towards early diagnosis and treatment of knee OA is warranted. However, there are no validated and widely accepted diagnostic criteria for early knee OA available. The current study aimed to take the first steps towards developing diagnostic criteria for early knee OA. METHODS: Data of 761 individuals with 1185 symptomatic knees at baseline were selected from the CHECK study. For CHECK, individuals with pain/stiffness of the knee, aged 45-65 years, who had no prior consultation or a first consultation with the general practitioner for these symptoms in the past 6 months were recruited and followed for 10 years. A group of 36 experts (17 general practitioners and 19 secondary care physicians) evaluated the medical records in pairs to diagnose the presence of clinically relevant knee OA 5-10 years after enrolment. A backward selection methods was used to create predictive models based on pre-defined baseline factors from history taking, physical examination, radiography and blood testing, using the experts' diagnoses as gold standard outcome. RESULTS: Prevalence of clinically relevant knee OA during follow-up was 37%. Created models contained 7-11 baseline factors and obtained an area under the curve between 0.746 (0.002) and 0.764 (0.002). CONCLUSION: The obtained diagnostic models for early knee OA had 'fair' predictive ability in individuals presenting with knee pain in primary care. Further modelling and validation of the identified predictive factors is required to obtain clinically feasible and relevant diagnostic criteria for early knee OA.
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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 | 0.017 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".