Catastrophic antiphospholipid syndrome: international consensus statement on classification criteria and treatment guidelines
Bibliographic record
Abstract
The term 'catastrophic' antiphospholipid syndrome (APS) is used to define an accelerated form of APS resulting in multiorgan failure. Although catastrophic APS patients represent less than 1% of all patients with APS, they are usually in a life-threatening medical situation that requires high clinical awareness. The careful and open discussion of several proposals by all participants in the presymposium workshop on APS consensus, held in Taormina on occasion of the 10th International Congress on aPL and chaired by Munther A Khamashta and Yehuda Shoenfeld (29 September 2002), has allowed the acceptation of a preliminary set of classification criteria. On the other hand, the optimal management of catastrophic APS must have three clear aims: to treat any precipitating factors (prompt use of antibiotics if infection is suspected, amputation for any necrotic organ, high awareness in patients with APS who undergo an operation or an invasive procedure), to prevent and to treat the ongoing thrombotic events and to suppress the excessive cytokine 'storm'. Anticoagulation (usually intravenous heparin followed by oral anticoagulants), corticosteroids, plasma exchange, intravenous gammaglobulins and, if associated with lupus flare, cyclophosphamide, are the most commonly used treatments for catastrophic APS patients.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".