Identification of Symptom Phenotypes of Hand Osteoarthritis Using Hierarchical Clustering: Results From the DIGICOD Cohort
Bibliographic record
Abstract
OBJECTIVE: We aimed to delineate phenotypes in hand osteoarthritis (HOA) based on cardinal symptoms (pain, functional limitation, stiffness, and aesthetic discomfort). METHODS: With data from the Digital Cohort Design (DIGICOD), we performed a hierarchical agglomerative clustering analysis based on Australian/Canadian Osteoarthritis Hand Index (AUSCAN) subscores for pain, physical function, stiffness, and visual analog scale for aesthetic discomfort. Kruskal-Wallis and post hoc analyses were used to assess differences between clusters. RESULTS: Among 389 patients, we identified 5 clusters: cluster 1 (n = 88) and cluster 2 (n = 91) featured low and mild symptoms; cluster 3 (n = 80) featured isolated aesthetic discomfort; cluster 4 (n = 42) featured a high level of pain, stiffness, and functional limitation; and cluster 5 (n = 88) had the same features as cluster 4 but with high aesthetic discomfort. For clusters 4 and 5, AUSCAN pain score was >41 of 100, representing only one-third of our patients. Aesthetic discomfort (clusters 3 and 5) was significantly associated with erosive HOA and a higher number of nodes. The highly symptomatic cluster 5 was associated but not significantly with metabolic syndrome, and body mass index and C-reactive protein level did not differ among clusters. Symptom intensity was significantly associated with joint destruction as well as with physical and psychological burden. Patients' main expectations differed among clusters, and function improvement was the most frequent expectation overall. CONCLUSION: The identification of distinct clinical clusters based on HOA cardinal symptoms suggests previously undescribed subtypes of this condition, warranting further study of biological characteristics of such clusters, and opening a path toward phenotype-based personalized medicine in HOA.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".