Damage measured by Damage Index for Antiphospholipid Syndrome (DIAPS) in antiphospholipid antibody-positive patients included in the APS ACTION registry
Bibliographic record
Abstract
OBJECTIVES: Our primary objective was to quantify damage burden measured by Damage Index for Antiphospholipid Syndrome (DIAPS) in aPL-positive patients with or without a history of thrombosis in an international cohort (the APS ACTION cohort). Secondly, we aimed to identify clinical and laboratory characteristics associated with damage in aPL-positive patients. METHODS: In this cross-sectional study, we analysed the baseline damage in aPL-positive patients with or without APS classification. We excluded patients with other autoimmune diseases. We analysed the demographic, clinical and laboratory characteristics based on two subgroups: (i) thrombotic APS patients with high vs low damage; and (ii) non-thrombotic aPL-positive patients with vs without damage. RESULTS: Of the 826 aPL-positive patients included in the registry as of April 2020, 586 with no other systemic autoimmune diseases were included in the analysis (412 thrombotic and 174 non-thrombotic). In the thrombotic group, hyperlipidaemia (odds ratio [OR] 1.82; 95% CI 1.05, 3.15; adjusted P = 0.032), obesity (OR 2.14; 95% CI 1.23, 3.71; adjusted P = 0.007), aβ2GPI high titres (OR 2.33; 95% CI 1.36, 4.02; adjusted P = 0.002) and corticosteroid use (ever) (OR 3.73; 95% CI 1.80, 7.75; adjusted P < 0.001) were independently associated with high damage at baseline. In the non-thrombotic group, hypertension (OR 4.55; 95% CI 1.82, 11.35; adjusted P = 0.001) and hyperlipidaemia (OR 4.32; 95% CI 1.37, 13.65; adjusted P = 0.013) were independent predictors of damage at baseline; conversely, single aPL positivity was inversely correlated with damage (OR 0.24; 95% CI 0.075, 0.77; adjusted P = 0.016). CONCLUSIONS: DIAPS indicates substantial damage in aPL-positive patients in the APS ACTION cohort. Selected traditional cardiovascular risk factors, steroids use and specific aPL profiles may help to identify patients more prone to present with a higher damage burden.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".