Platelet Transfusions in Pediatric Intensive Care*
Bibliographic record
Abstract
OBJECTIVES: To characterize the determinants of platelet transfusion in a PICU and determine whether there exists an association between platelet transfusion and adverse outcomes. DESIGN: Prospective observational single center study, combined with a self-administered survey. SETTING: PICU of Sainte-Justine Hospital, a university-affiliated tertiary care institution. PATIENTS: All children admitted to the PICU from April 2009 to April 2010. INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: Among 842 consecutive PICU admissions, 60 patients (7.1%) received at least one platelet transfusion while in PICU. In the univariate analysis, significant determinants for platelet transfusion were admission Pediatric Risk of Mortality Score greater than 10 (odds ratio, 6.80; 95% CI, 2.5-18.3; p < 0.01) and Pediatric Logistic Organ Dysfunction scores greater than 20 (odds ratio, 26.9; 95% CI, 8.88-81.5; p < 0.01), history of malignancy (odds ratio, 5.08; 95% CI, 2.43-10.68; p < 0.01), thrombocytopenia (platelet count, < 50 × 10/L or < 50,000/mm) (odds ratio, 141; 95% CI, 50.4-394.5; p < 0.01), use of heparin (odds ratio, 3.03; 95% CI, 1.40-6.37; p < 0.01), shock (odds ratio, 5.73; 95% CI, 2.85-11.5; p < 0.01), and multiple organ dysfunction syndrome (odds ratio, 10.41; 95% CI, 5.89-10.40; p < 0.01). In the multivariate analysis, platelet count less than 50 × 10/L (odds ratio, 138; 95% CI, 42.6-449; p < 0.01) and age less than 12 months (odds ratio, 3.06; 95% CI, 1.03-9.10; p = 0.02) remained statistically significant determinants. The attending physicians were asked why they gave a platelet transfusion; the most frequent justification was prophylactic platelet transfusion in presence of thrombocytopenia with an average pretransfusion platelet count of 32 ± 27 × 10/L (median, 21), followed by active bleeding with an average pretransfusion platelet count of 76 ± 39 × 10/L (median, 72). Platelet transfusions were associated with the subsequent development of multiple organ dysfunction syndrome (odds ratio, 2.53; 95% CI, 1.18-5.43; p = 0.03) and mortality (odds ratio, 10.1; 95% CI, 4.48-22.7; p < 0.01). CONCLUSIONS: Among children, 7.1% received at least one platelet transfusion while in PICU. Thrombocytopenia and active bleeding were significant determinants of platelet transfusion. Platelet transfusions were associated with the development of multiple organ dysfunction syndrome and increased mortality.
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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 | 0.000 | 0.005 |
| 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 | 0.000 | 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".