Diagnosis for early stage knee osteoarthritis: probability stratification, internal and external validation; data from the CHECK and OAI cohorts
Bibliographic record
Abstract
OBJECTIVE: To internally and externally validate our diagnostic criteria of early stage knee osteoarthritis (OA) in the CHECK and OAI cohorts. DESIGN: We applied two previously developed diagnostic models to all knees in CHECK and OAI cohorts to calculate probabilities of early stage knee OA at baseline. Knees were categorized into three groups based on probability: 'no OA' (probability ≤ 30%), 'uncertain' (probability between 30% and 70%) and 'early stage OA' (probability ≥ 70%). To validate the diagnosis, we obtained OA related outcome measures at 10-year follow-up in the CHECK cohort, and at 8-9-year follow-up in the OAI cohort. We compared outcome measures between 'no OA' and 'early stage OA' knees, and between 'no OA' and 'uncertain' knees using generalized estimating equations. RESULTS: In CHECK (n = 1042 knees) both models showed 'early stage OA' knees presented with significant and clinically relevant higher WOMAC scores, higher Kellgren & Lawrence (KL) grade, and higher rates of joint space narrowing (JSN) progression after 10 years, compared to 'no OA' knees. In OAI (n = 2937 knees) both models showed 'early stage OA' knees presented with significant and clinically relevant higher WOMAC scores, higher KL grade, and higher rates of KL and JSN progression after 8-9 years, compared to 'no OA' knees. Smaller, but still significant differences between 'uncertain' and 'no OA' knees were observed in both cohorts. CONCLUSIONS: These results support internal and external validity of the two sets of diagnostic criteria for early stage 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 | 0.020 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".