Bibliographic record
Abstract
Heparin-induced thrombocytopenia (HIT) is a prothrombotic drug reaction caused by platelet-activating IgG antibodies that recognize platelet factor 4 (PF4)/polyanion complexes. Platelet activation assays, such as the serotonin-release assay, are superior to PF4-dependent immunoassays in discerning which heparin-induced antibodies are clinically relevant. When HIT is strongly suspected, standard practice includes substituting heparin with an alternative anticoagulant; the 2 US-approved agents are the direct thrombin inhibitors (DTIs) lepirudin and argatroban, which are "niche" agents used only to manage HIT. However, only ~ 10% of patients who undergo serological investigation for HIT actually have this diagnosis. Indeed, depending on the clinical setting, only 10%-50% of patients with positive PF4-dependent immunoassays have platelet-activating antibodies. Therefore, overdiagnosis of HIT can be minimized by insisting that a positive platelet activation assay be required for definitive diagnosis of HIT. For these reasons, a management strategy that considers the real possibility of non-HIT thrombocytopenia is warranted. One approach that I suggest is to administer an indirect, antithrombin (AT)-dependent factor Xa inhibitor (danaparoid or fondaparinux) based upon the following rationale: (1) effectiveness in treating and preventing HIT-associated thrombosis; (2) effectiveness in treating and preventing thrombosis in diverse non-HIT situations; (3) both prophylactic- and therapeutic-dose protocols exist, permitting dosing appropriate for the clinical situation; (4) body weight-adjusted dosing protocols and availability of specific anti-factor Xa monitoring reduce risk of under- or overdosing (as can occur with partial thromboplastin time [PTT]-adjusted DTI therapy); (5) their long half-lives reduce risk of rebound hypercoagulability; (6) easy coumarin overlap; and (7) relatively low cost.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 0.016 |
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".