Longitudinal stability of molecular endotypes of knee osteoarthritis patients
Bibliographic record
Abstract
OBJECTIVE: To assess the longitudinal stability of biomarker-based molecular endotypes of knee osteoarthritis (KOA) participants from APPROACH and to evaluate the consistency of findings in an independent KOA population. METHODS: Nineteen biomarkers were measured longitudinally in 295 KOA participants from the APPROACH cohort. K-means clustering was used to identify the structural damage, inflammation, and low tissue turnover endotypes at the six-, 12-, and 24-month follow-ups. Endotype stability was defined as having the same independent endotype assignment longitudinally for patients with complete data (n = 226). Clinical and biochemical characteristics were compared between participants with longitudinally stable and unstable endotypes. The presence and longitudinal stability of the endotypes were evaluated in a different KOA population from the placebo arm of the oral salmon calcitonin trials. RESULTS: An average overall longitudinal endotype stability of 55% (Fleiss' Kappa of 0.53; 95% confidence interval [CI]: 0.46, 0.60) was demonstrated. An average stability of 59% (range: 54-59%) was observed for the structural damage endotype (Fleiss' Kappa 0.52; 95% CI: 0.45, 0.60), 54% (52-56%) for the inflammatory (Fleiss' Kappa 0.61; 95% CI: 0.53, 0.68), and 50% (49-52%) for the low tissue turnover endotype (Fleiss' Kappa 0.46; 95% CI: 0.39, 0.54). Participants with longitudinally unstable endotypes exhibited molecular properties of more than one endotype, which were detectable already at the first visit. CONCLUSIONS: Our study showed for the first time that more than half of KOA participants exhibited a longitudinally stable endotype, highlighting the applicability of biomarker-based endotyping in a clinical trial setting.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".