Systematic review of fondaparinux for heparin‐induced thrombocytopenia: When there are no randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Fondaparinux is commonly used for treatment of heparin-induced thrombocytopenia (HIT) despite lack of approval for this indication. High quality randomized controlled trials of this agent are unlikely to be forthcoming. OBJECTIVES: The objective of this systematic review is to update the literature on the efficacy and safety of fondaparinux for treatment of confirmed and probable HIT based on the available evidence. METHODS: Primary articles were identified using Web of Science and PubMed database searches for English-language studies from January 2006 to November 2017. Selected studies enrolled consecutive adult patients who received fondaparinux as the primary anticoagulant to treat acute HIT; confirmed the diagnosis by serological testing with a serotonin-release assay; heparin-induced platelet activation assay or enzyme-linked immunosorbent assay; provided clinical criteria used to define HIT and reported clinically important outcomes. RESULTS: A total of 9 studies were identified with 154 HIT positive patients. Ten experienced a new thrombotic event while receiving fondaparinux (6.5%, 95% CI, 3.4 to 11.7%) and 26 experienced major bleeding (16.9%, 95% CI, 11.7 to 23.6%). Mortality due to thrombosis or bleeding was reported in 5 patients (3.2%, 95% CI, 1.2 to 7.6%). CONCLUSIONS: Fondaparinux appears to be an effective and safe anticoagulant for treatment of acute HIT despite the absence of randomized trials. Caution should exercised when using fondaparinux in patients with renal insufficiency.
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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 | 0.027 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.012 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".