Bibliographic record
Abstract
Antiphospholipid syndrome (APS) is an acquired autoimmune condition characterized by thrombotic events, pregnancy morbidity, and laboratory evidence of antiphospholipid antibodies (aPL). Management of these patients includes the prevention of a first thrombotic episode in at-risk patients (primary prevention) and preventing recurrent thrombotic complications in patients with a history of thrombosis (secondary prevention). Assessment of thrombotic risk in these patients, balanced against estimated bleeding risks associated with antithrombotic therapy could assist clinicians in determining whether antithrombotic therapy is warranted. Thrombotic risk can be assessed by evaluating a patient's aPL profile and additional thrombotic risk factors. Although antithrombotic options for secondary prevention of venous thromboembolism (VTE) have been evaluated in clinical trials, studies in primary prevention of asymptomatic aPL-positive patients are needed. Primary prevention with aspirin may be considered in asymptomatic patients who have a high-risk aPL profile, particularly if additional risk factors are present. Secondary prevention with long-term anticoagulation is recommended based on estimated risks of VTE recurrence, although routine evaluation of thrombotic risk can assist in determining whether ongoing anticoagulation is warranted. Studies that stratify thrombotic risk in aPL-positive patients, and patients with APS evaluating antithrombotic and non-antithrombotic therapies will be useful in optimizing the management of these patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".