Anemia at pediatric intensive care unit discharge: prevalence and risk markers
Bibliographic record
Abstract
BACKGROUND: Anemia is prevalent at pediatric intensive care unit (PICU) admission and incident during PICU stay, but little is known about anemia at PICU discharge . Anemia after critical illness is an important issue because it could impact post-PICU outcome. We aimed to estimate the prevalence of anemia at PICU discharge and to determine its risk markers. METHODS: This is an ancillary study of a prospective observational study on transfusion practices conducted in the PICU of a tertiary care children's hospital. All children consecutively admitted to the PICU during a 1-year period were considered for inclusion. Data were prospectively collected from medical charts, except for hemoglobin (Hb) levels at PICU and hospital discharge that were collected retrospectively. Anemia was defined by an Hb concentration below the lower limit of the normal range for age. RESULTS: Among the 679 children retained for analysis, 390 (57.4%) were anemic at PICU discharge. After multivariate adjustment, anemia at PICU admission was the strongest risk marker of anemia at PICU discharge. The strength of this association varied according to age (interaction): The odds ratio (OR) (95% CI) of anemia at PICU discharge was 4.85 (1.67-14.11) for 1-5-month-old infants anemic versus not anemic at PICU admission, and it was 73.13 (13.43, 398.19) for adolescents anemic versus not anemic at PICU admission. Children admitted after a non-cardiac surgery had an increased risk of anemia at PICU discharge [OR 2.30 (1.37, 3.88), p = 0.002]. The proportion of anemic children differed between age categories, while the median Hb level did not exhibit significant variations according to age. CONCLUSIONS: Anemia is highly prevalent at PICU discharge and is strongly predicted by anemia at PICU admission. The usual age-based definitions of anemia may not be relevant for critically ill children. The consequences of anemia at PICU discharge are unknown and deserve further scrutiny.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".