ANA-positive <i>versus</i> ANA-negative Antiphospholipid Antibody-positive Patients: Results from the Antiphospholipid Syndrome Alliance for Clinical Trials and International Networking (APS ACTION) Clinical Database and Repository (“Registry”)
Bibliographic record
Abstract
OBJECTIVES: This study focused on the prevalence and impact of ANA in aPL-positive patients without concomitant systemic autoimmune rheumatic diseases (SARDs). METHODS: Data from aPL-positive patients with or without Revised Sapporo APS classification criteria were retrieved from the Antiphospholipid Syndrome Alliance for Clinical Trials and International Networking (APS ACTION) Registry. Patients with concomitant SARDs were excluded. RESULTS: A total of 430 aPL-positive patients were included in the analysis, 56% ANA-positive (ANA+) and 44% ANA-negative (ANA-). ANA positivity was significantly associated with history of haematologic manifestations (persistent autoimmune haemolytic anaemia, thrombocytopenia, leukopenia and/or lymphopenia) (16% of ANA+ vs 7% of ANA-, P = 0.006). Triple aPL-positivity was more frequent in the ANA+ subgroup (P = 0.02), along with low baseline C3 and C4 levels (P = 0.05 and P = 0.009, respectively), and higher frequency for ENA. Among aPL-positive patients with no APS classification, ANA+ patients showed a higher rate of arthritis (P = 0.006). Among female patients who have experienced at least one pregnancy, 113 were ANA+ and 96 were ANA-; ANA- patients had a higher number of pregnancies (P = 0.018), and number of live births (P = 0.014). A wider proportion of ANA+ patients were treated with HCQ (P < 0.001). CONCLUSION: When we analysed aPL-positive patients with no other SARDs, ANA status was not associated with thrombosis or pregnancy morbidity. Interestingly, ANA+ patients showed higher rates of systemic autoimmune features, including haematologic manifestations, multiple aPL positivity, lower complement levels, ENA positivity, and joint involvement, and were more often treated with HCQ. Finally, aPL-positive subjects who were ANA- had a higher rate of pregnancies and live births.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".