The association between platelet transfusions and bleeding in critically ill patients with thrombocytopenia
Bibliographic record
Abstract
Background Platelet transfusions are commonly used to treat critically ill patients with thrombocytopenia. Whether platelet transfusions are associated with a reduction in the risk of major bleeding is unknown. Patients/Methods Observational cohort study nested in a previous multicenter, randomized thromboprophylaxis trial in the intensive care unit (ICU). The objective was to evaluate the association between platelet transfusions and adjudicated major bleeding events. Platelet transfusion episodes were reviewed for timing of administration, product type, and dose. Major bleeding with and without platelet transfusions was adjusted for severity of thrombocytopenia, use of anti‐platelet agents, surgery and other covariates. Secondary outcomes were thrombosis, death in ICU and platelet count increment. Results Among 2,256 patients, 71 (3.1%) received 190 platelet transfusions. Of those, 121 (63.7%) were administered to 54 non‐bleeding, thrombocytopenic patients. Adjusted rates of major bleeding were not statistically different with or without the administration of platelet transfusions (hazard ratio for transfused patients 0.85; 95% confidence interval, 0.42‐1.72). We did not find a significant association between platelet transfusion use and thrombosis or death in ICU in adjusted analyses. Thrombocytopenia, anemia, major or minor bleeding and use of anticoagulants were associated with platelet transfusion administration. The median post‐transfusion platelet count increment was 20×10 9 /L at 3.5 hours post‐transfusion. Conclusions Rates of major bleeding were not different for patients who did and did not receive platelet transfusions. Inferences were limited by the small number of transfused patients. Clinical trials are needed to better investigate the potential hemostatic benefit and potential harms of platelet transfusions for this high‐risk population.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".