Identifying Phenotypes of Knee Osteoarthritis by Separate Quantitative Radiographic Features May Improve Patient Selection for More Targeted Treatment
Bibliographic record
Abstract
OBJECTIVE: Expression of osteoarthritis (OA) varies significantly between individuals, and over time, suggesting the existence of different phenotypes, possibly with specific etiology and targets for treatment. Our objective was to identify phenotypes of progression of radiographic knee OA using separate quantitative features. METHODS: Separate radiographic features of OA were measured by Knee Images Digital Analysis (KIDA) in individuals with early knee OA (the CHECK cohort: Cohort Hip & Cohort Knee), at baseline and at 2-year and 5-year followup. Hierarchical clustering was performed to identify phenotypes of radiographic knee OA progression. The phenotypes identified were compared for changes in joint space width (JSW), varus angle, osteophyte area, eminence height, bone density, for Kellgren-Lawrence (K-L) grade, and for clinical characteristics. Logistic regression analysis evaluated whether baseline radiographic features and demographic/clinical characteristics were associated with each of the specific phenotypes. RESULTS: The 5 clusters identified were interpreted as "Severe" or "No," "Early" or "Late" progression of the radiographic features, or specific involvement of "Bone density." Medial JSW, varus angle, osteophyte area, eminence height, and bone density at baseline were associated with the Severe and Bone density phenotypes. Lesser eminence height and bone density were associated with Early and Late progression. Larger varus angle and smaller osteophyte area were associated with No progression. CONCLUSION: Five phenotypes of radiographic progression of early knee OA were identified using separate quantitative features, which were associated with baseline radiographic features. Such phenotypes might require specific treatment and represent relevant subgroups for clinical trials.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".