An evidence-based threshold for thrombocytopenia associated with clinically significant bleeding in pediatric intensive care unit patients*
Bibliographic record
Abstract
OBJECTIVE: To determine the epidemiology and identify the risk factors for clinically significant bleeding in the pediatric intensive care unit. DESIGN: A retrospective cohort study over 6 months with up to 7 days of observation for each patient. SETTING: The pediatric intensive care unit in a tertiary care children's hospital. PATIENTS: Three hundred twenty-six consecutive patients admitted to the pediatric intensive care unit during the study period, with 214 eligible for inclusion. MEASUREMENTS AND MAIN RESULTS: Clinically significant bleeding, defined using a composite of outcomes. Clinically significant bleeding occurred in 19 patients (8.9%). Recursive partitioning identified a platelet count <100 × 10/L as being associated with clinically significant bleeding. Other factors associated with increased risk included mechanical ventilation, antibiotic and antacid medications, the performance of multiple procedures, and cardiac surgery. Episodes of clinically significant bleeding were observed at a median of 9.8 hrs after admission. CONCLUSIONS: Clinically significant bleeding is a more common complication for pediatric intensive care unit patients than has been previously reported. The evidence-based threshold for thrombocytopenia identified as a risk factor should be further investigated in a prospective study.
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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 | 0.011 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".