Association between in-ICU red blood cells transfusion and 1-year mortality in ICU survivors
Bibliographic record
Abstract
BACKGROUND: Impact of in-ICU transfusion on long-term outcomes remains unknown. The purpose of this study was to assess in critical-care survivors the association between in-ICU red blood cells transfusion and 1-year mortality. METHODS: FROG-ICU, a multicenter European study enrolling all-comers critical care patients was analyzed (n = 1551). Association between red blood cells transfusion administered in intensive care unit and 1-year mortality in critical care survivors was analyzed using an augmented inverse probability of treatment weighting-augmented inverse probability of censoring weighting method to control confounders. RESULTS: Among the 1551 ICU-survivors, 42% received at least one unit of red blood cells while in intensive care unit. Patients in the transfusion group had greater severity scores than those in the no-transfusion group. According to unweighted analysis, 1-year post-critical care mortality was greater in the transfusion group compared to the no-transfusion group (hazard ratio (HR) 1.78, 95% CI 1.45-2.16). Weighted analyses including 40 confounders, showed that transfusion remained associated with a higher risk of long-term mortality (HR 1.21, 95% CI 1.06-1.46). CONCLUSIONS: Our results suggest a high incidence of in-ICU RBC transfusion and that in-ICU transfusion is associated with a higher 1-year mortality among in-ICU survivors. Trial registration ( NCT01367093 ; Registered 6 June 2011).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".