Bibliographic record
Abstract
Antiphospholipid antibodies are a heterogenous group of autoantibodies directed against glycoproteins in concert with anionic phospholipids. In clinical laboratory practice, antiphospholipid antibody evaluations usually consist of a combination of the following: anticardiolipin antibody assay, anti-beta 2 glycoprotein I assay, and at least two lupus anticoagulant assays with an appropriate confirmatory test. Lupus anticoagulants produce their laboratory effect by prolonging recalcification times in assays within which phospholipid content is limited. Although many assays are available, all are based on the fundamental principle of demonstrating normalization of prolonged recalcification times with the addition of exogenous phospholipid. The antibody specificity of an individual lupus anticoagulant is difficult or impossible to determine; however a small proportion do demonstrate avidity for selected proteins such as prothrombin or beta 2 glycoprotein I. The mechanism by which these antibodies cause their clinical manifestations remains unknown; however their relationship to increased risk of thrombosis, pregnancy loss, and autoimmune thrombocytopenia is undoubted. There is no correlation between the "strength" of lupus anticoagulants and the level of thrombotic risk; thus it is important to identify both "weak" and "strong" lupus anticoagulants.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.015 |
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".