The meaning of non-criteria clinical manifestations in a real-life primary antiphospholipid syndrome cohort
Bibliographic record
Abstract
OBJECTIVES: We aimed to evaluate the prevalence of non-criteria clinical features in patients with primary antiphospholipid syndrome (APS), and to assess their relationship to thrombosis and damage. METHODS: We retrospectively included 177 primary APS patients, and/or patients who only achieved the serological Sydney criteria but had thrombocytopenia and/or haemolytic anaemia. We registered demographics, serology, treatment, thrombotic/obstetric manifestations and non-criteria clinical manifestations (cutaneous, haematologic, renal, heart valve disease, and neurological). We scored the DIAPS and a modified SLICC index. We used logistic regression and reported OR with 95% CI. RESULTS: 78% were women with a median follow-up of 6.7 years. Thrombosis was found in 74% of patients, 29.3% had obstetric features, and 64% had non-criteria clinical manifestations. The frequency of the non-criteria clinical manifestation was: haematologic 40.1%, cutaneous 20.9%, neurologic 18%, cardiac 5% and renal 4.5%. Non-criteria features were associated with LA (OR 2.3, 95% 1.03-5.1) and prednisone use (OR 8.2, 95% CI 1.7-39.3). A DIAPS score ≥1 was associated with thrombosis (OR 53.1, 95% CI 17.8-15.2), prednisone use (OR 0.27, CI 95% 0.09-0.83) and neurological involvement (OR 6.4, 95% CI 1.05-39.8); whereas a modified SLICC ≥ 1 with thrombosis (OR 10.2; IC 95% 4.43-26.1), neurological involvement (OR 6.4, 95%CI 1.05-39.8), obstetric features (OR 0.32 CI 95% 0.12-0,81) and cutaneous features (OR 5.3, CI 95% 1.4-19), especially livedo reticularis (OR 5.45; IC 95% 1.49-19.8). CONCLUSIONS: Non-criteria clinical manifestations are common and associated with LA. Among them, neurologic involvement and the presence of livedo were associated with damage accrual.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".