The role of direct oral anticoagulants (DOACs) in the treatment of heparin-induced thrombocytopenia (HIT): An evidence-based literature review
Bibliographic record
Abstract
Heparin-induced thrombocytopenia (HIT) poses a risk of death secondary to thrombotic complications.Treatment options are limited for patients with poor IV access, as contemporary options are restricted to parenteral agents before switching to oral vitamin k antagonists. A literature review was conducted to examine the effectiveness of direct oral anticoagulants (DOACs) in the primary treatment of HIT. High quality evidence is scarce surrounding the use of DOACs for this indication, while past reviews have not critically appraised the evidence. Additionally, the most recent study from 2017 investigating the use of DOACs for this indication has not been reported in past literature reviews.The Cochrane Library, Embase, PubMed, Google Scholar and ClinicalTrials.gov were searched to identify and critically appraise the best available evidence. Salient literature demonstrates that DOACs are effective at raising platelet count to baseline after seven days, on average.Thrombosis and major bleeding are rarely observed when DOACs are used as primary therapy. While large scale studies are needed, patients with HIT that have poor IV access may benefit from the ease of administration, rapid onset of action and lack of routine monitoring associated with DOAC therapy.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".