The association between platelet transfusions and mortality in patients with critical illness
Bibliographic record
Abstract
BACKGROUND: Platelet (PLT) transfusions are frequently administered in the setting of critical illness but their clinical impacts remain unknown. This study examined the association between PLT transfusions and death in a large intensive care unit (ICU) patient population. STUDY DESIGN AND METHODS: Using a transfusion registry spanning 2008 to 2015, this study assessed effect of in-ICU PLT transfusions on ICU and in-hospital mortality using a stratified, time-dependent Cox proportional hazards model adjusted for illness severity, thrombocytopenia, and other confounders. Patients with known malignancy were excluded. Exposure to PLT transfusions were analyzed dichotomously (ever or never transfused) and continuously (number of transfusions). Medical, general surgery, and cardiac surgery subgroups were analyzed separately. RESULTS: Overall 32,842 adult patients were admitted to ICU, and 4927 patients received PLT transfusion(s). Crude in-ICU and in-hospital mortality were higher for PLT-transfused patients compared to nontransfused patients (9.2% vs. 6.7% and 12.3% vs. 9.3%, respectively). After confounders were adjusted for, PLT transfusions (ever vs. never) were not associated with increased mortality in ICU (hazard ratio [HR], 0.78; 95% confidence interval [CI], 0.60-1.02; p = 0.06) or in hospital (HR, 0.89; 95% CI, 0.68-1.09; p = 0.41). Continuous exposure analysis also showed no association between PLT transfusions and death. PLT transfusions have a protective effect on in-hospital mortality in the subgroup of general surgery patients (HR, 0.71; 95% CI, 0.51-0.99; p = 0.04; ever or never analysis). CONCLUSION: Platelet transfusions were not associated with increased risk of death in critically ill patients. Further studies are required to identify subgroups for which PLT transfusions may be beneficial.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".