Cluster analysis for the identification of clinical phenotypes among antiphospholipid antibody-positive patients from the APS ACTION Registry
Bibliographic record
Abstract
OBJECTIVE: This study aimed to use cluster analysis (CA) to identify different clinical phenotypes among antiphospholipid antibodies (aPL)-positive patients. METHODS: The Alliance for Clinical Trials and International Networking (APS ACTION) Registry includes persistently positive aPL of any isotype based on the Sydney antiphospholipid syndrome (APS) classification criteria. We performed CA on the baseline characteristics collected retrospectively at the time of the registry entry of the first 500 patients included in the registry. A total of 30 clinical data points were included in the primary CA to cover the broad spectrum of aPL-positive patients. RESULTS: A total of 497 patients from international centres were analysed, resulting in three main exclusive clusters: (a) female patients with no other autoimmune diseases but with venous thromboembolism (VTE) and triple-aPL positivity; (b) female patients with systemic lupus erythematosus, VTE, aPL nephropathy, thrombocytopaenia, haemolytic anaemia and a positive lupus anticoagulant test; and (c) older men with arterial thrombosis, heart valve disease, livedo, skin ulcers, neurological manifestations and cardiovascular disease (CVD) risk factors. CONCLUSIONS: Based on our hierarchical cluster analysis, we identified different clinical phenotypes of aPL-positive patients discriminated by aPL profile, lupus or CVD risk factors. Our results, while supporting the heterogeneity of aPL-positive patients, also provide a foundation to understand disease mechanisms, create new approaches for APS classification and ultimately develop new management approaches.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".