Bibliographic record
Abstract
References Lanthier S, Kirkham FJ, Mitchell LG et al (2004) Increased anticardiolipin antibody IgG titers do not predict recurrent stroke or TIA in children. Neurology 62:194–200 CAS PubMed Google Scholar Lockshin MD, Erkan D (2003) Treatment of the antiphospholipid syndrome. N Engl J Med 349:1177–1179 CAS PubMed Google Scholar Rand JH (2007) The antiphospholipid syndrome. Hematology 2007(1):136–142 Google Scholar Roach ES, Golomb MR, Adams R et al (2008) Management of stroke in infants and children: a scientific statement from a Special Writing Group of the American Heart Association Stroke Council and the Council on Cardiovascular Disease in the Young. Stroke 39(9):2644–2691 PubMed Google Scholar Suggested Reading Cervera R, Khamashta MA, Shoenfeld Y et al (2009) Morbidity and mortality in the antiphospholipid syndrome during a 5-year period: a multicentre prospective study of 1000 patients. Ann Rheum Dis 68(9):1428–1432 CAS PubMed Google Scholar Giannakopoulos B, Krilis SA (2009) How I treat the antiphospholipid syndrome. Blood 114:2020–2030 CAS PubMed Google Scholar Giannakopoulos B, Passam F, Rahgozar S et al (2007) Current concepts on the pathogenesis of the antiphospholipid syndrome. Blood 109:422–430 CAS PubMed Google Scholar Tarr T, Lakos G, Bhattoa HP et al (2007) Analysis of risk factors for the development of thrombotic complications in antiphospholipid antibody positive lupus patients. Lupus 16:39–45 CAS PubMed Google Scholar Download references
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".