International Study of the Epidemiology of Platelet Transfusions in Critically Ill Children With an Underlying Oncologic Diagnosis
Bibliographic record
Abstract
OBJECTIVES: To describe the epidemiology of platelet transfusions in critically ill children with an underlying oncologic diagnosis and to examine effects of prophylactic versus therapeutic transfusions. DESIGN: Subgroup analysis of a prospective, observational study. SETTING: Eighty-two PICUs in 16 countries. PATIENTS: All children (3 d to 16 yr old) who received a platelet transfusion during one of the six predefined screening weeks and had received chemotherapy in the previous 6 months or had undergone hematopoietic stem cell transplantation in the last year. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of the 548 patients enrolled in the parent study, 237 (43%) had an underlying oncologic diagnosis. In this population, 71% (168/237) of transfusions were given prophylactically, and 59% (139/237) of transfusions were given at a total platelet count greater than 20 × 10/L, higher than the current recommendations. Those with an underlying oncologic diagnosis were significantly older, and received less support including less mechanical ventilation, fewer medications that affect platelet function, and less use of extracorporeal life support than those without an underlying oncologic diagnosis. In this subpopulation, there were no statistically significant differences in median (interquartile range) platelet transfusion thresholds when comparing bleeding or nonbleeding patients (50 × 10/L [10-50 × 10/L] and 30 × 10/L [10-50 × 10/L], respectively [p = 0.166]). The median (interquartile range) interval transfusion increment in children with an underlying oncologic diagnosis was 17 × 10/L (6-52 × 10/L). The presence of an underlying oncologic diagnosis was associated with a poor platelet increment response to platelet transfusion in this cohort (adjusted odds ratio, 0.46; 95% CI, 0.22-0.95; p = 0.035). CONCLUSIONS: Children with an underlying oncologic diagnosis receive nearly half of platelet transfusions prescribed by pediatric intensivists. Over half of these transfusions are prescribed at total platelet count greater than current recommendations. Studies must be done to clarify appropriate indications for platelet transfusions in this vulnerable 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".