How 217 Pediatric Intensivists Manage Anemia at PICU Discharge: Online Responses to an International Survey*
Bibliographic record
Abstract
OBJECTIVE: To describe the management of anemia at PICU discharge by pediatric intensivists. DESIGN: Self-administered, online, scenario-based survey. SETTING: PICUs in Australia/New Zealand, Europe, and North America. SUBJECTS: Pediatric intensivists. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Respondents were asked to report their decisions regarding RBC transfusions, iron, and erythropoietin prescription to children ready to be discharged from PICU, who had been admitted for hemorrhagic shock, cardiac surgery, craniofacial surgery, and polytrauma. Clinical and biological variables were altered separately in order to assess their effect on the management of anemia. Two-hundred seventeen responses were analyzed. They reported that the mean (± SEM) transfusion threshold was a hemoglobin level of 6.9 ± 0.09 g/dL after hemorrhagic shock, 7.6 ± 0.10 g/dL after cardiac surgery, 7.0 ± 0.10 g/dL after craniofacial surgery, and 7.0 ± 0.10 g/dL after polytrauma (p < 0.001). The most important increase in transfusion threshold was observed in the presence of a cyanotic heart disease (mean increase ranging from 1.80 to 2.30 g/dL when compared with baseline scenario) or left ventricular dysfunction (mean increase, 1.41-2.15 g/dL). One third of respondents stated that they would not prescribe iron at PICU discharge, regardless of the hemoglobin level or the baseline scenario. Most respondents (69.4-75.0%, depending on the scenario) did not prescribe erythropoietin. CONCLUSIONS: Pediatric intensivists state that they use restrictive transfusion strategies at PICU discharge similar to those they use during the acute phase of critical illness. Supplemental iron is less frequently prescribed than RBCs, and prescription of erythropoietin is uncommon. Optimal management of post-PICU anemia is currently unknown. Further studies are required to highlight the consequences of this anemia and to determine appropriate management.
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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 | 0.003 | 0.012 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".