The use of physiologic markers of anemia intolerance to guide transfusion practice in pediatric intensive care units: an international survey
Bibliographic record
Abstract
Abstract Objectives To explore how red blood cell (RBC) transfusion practice of pediatric intensivists is modulated by physiologic markers of anemia intolerance, in addition to the hemoglobin (Hb) concentration. Background Most research to date has tested transfusion policies based on Hb threshold alone. Use of physiologic parameters to guide RBC transfusion in pediatric intensive care units (PICU) is not well described. Methods/materials Scenario-based self-administered survey among pediatric intensivists in tertiary-care PICUs in Belgium, Canada, France, Japan and United Kingdom. Pediatric intensivists were approached through national networks and by e-mail. Five case scenarios were developed for non-bleeding critically ill children who were hemodynamically stable at baseline. Respondents were asked to select a Hb threshold for each scenario and indicate how alternative thresholds of different physiologic parameters would modify their baseline hemoglobin (Hb) threshold. Results One hundred thirty-two participant responses were received (response rate 56%). Findings indicate that pediatric intensivists do incorporate physiologic parameters when deciding to transfuse RBCs. The most significant determinants of RBC transfusion, in addition to Hb threshold, were baseline co-morbidity (cyanotic cardiac vs. other patients), ScvO2, blood lactate, increasing inotrope or vasoactive-inotropic score, and a drop ≥ 3 g/dL in the Hb concentration. Conclusions Stated transfusion practice by pediatric intensivists involves physiologic parameters, in addition to Hb concentration. Both static and dynamic parameters (single value and trend over time) were considered by clinicians. The striking variation in practice pattern reported strongly supports the need for further studies that would identify and assess the impact of physiologic biomarkers for RBC transfusion in PICU.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".