Predictors of Mortality in Antiphospholipid Antibody–Positive Patients: Prospective Results From Antiphospholipid Syndrome Alliance for Clinical Trials and International Networking Clinical Database and Repository
Bibliographic record
Abstract
OBJECTIVE: The objective was to determine the mortality rate as well as the causes and predictors of death in antiphospholipid antibody (aPL)-positive patients with and without antiphospholipid syndrome (APS) classification. METHODS: The inclusion criterion for the multicenter international Antiphospholipid Syndrome Alliance for Clinical Trials and International Networking (APS ACTION) registry is positive aPLs according to the Revised Sapporo Classification Criteria tested within one year before enrollment. Patients are observed every 12 ± 3 months with clinical data and blood collection. For this prospective analysis, we first analyzed the causes of death for patients reported as "deceased." Secondly, we analyzed risk factors for death using the adjusted Cox proportional hazards model and calculated survival probability using the Kaplan-Meier model based on different age groups. RESULTS: Of 967 patients, 43 (5%) were deceased after a median follow-up of 5.3 years. Based on the univariate analysis, deceased patients, compared to living patients, were more likely to be older and have a history of arterial thrombosis, catastrophic APS, concomitant systemic autoimmune diseases (SAIDs), and baseline cardiovascular disease (CVD) risk factors. Based on the Cox proportional hazards model adjusted for age and for each of the strongest predictors of death, arterial thrombosis (hazard ratio [HR] 2.94, 95% confidence interval [CI] 1.50-5.76), concomitant SAIDs (HR 2.97, 95% 1.56-5.63), and baseline any CVD risk factor (HR 2.43, 95% CI 1.05-5.71) were significantly associated with mortality. CONCLUSION: In our cohort of persistently aPL-positive patients, the mortality rate was 5% after a median follow-up of five years and was highest for patients ≥60 years old at registry entry. History of arterial thrombosis, concomitant SAIDs, and baseline any CVD risk factor independently predicted future death.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".