Association between the length of storage of transfused leukoreduced red blood cell units and hospital‐acquired infections in critically ill children: A secondary analysis of the <scp>TRIPICU</scp> study
Bibliographic record
Abstract
OBJECTIVE: Evaluate the association between leukoreduced red blood cell (RBC) storage length and hospital-acquired infection (HAI) incidence rate in critically ill children. BACKGROUND: RBC transfusions are common in critically ill children. Despite their benefits, observational studies suggest an association between them and HAIs. One possible mechanism for increased HAI is transfusion-related immunomodulation due to bioactive substances' release as transfused blood ages. METHODS: In this secondary analysis of the 'Transfusion Requirement in Paediatric Intensive Care Units' (TRIPICU) study, we analysed a subset of 257 participants that received only one pre-storage leukoreduced RBC transfusion. RBC storage length was classified as 1) transfusion of 'fresh' RBCs (≤10 days), 2) transfusion of 'stored' RBCs (21-34 days), and 3) transfusion of 'long-stored' RBCs (≥35 days). All were compared to a 'golden' period (11-20 days), representing the time between 'fresh' and 'stored'. We used quasi-Poisson multivariable regression models to estimate the HAI incidence rate ratio (IRR) and corresponding 95% confidence interval (CI). RESULTS: We found that the association between the length of storage time of leukoreduced RBCs and HAIs was not significant in the 'fresh' group (IRR 1.23; 95% CI 0.55, 2.78) and the 'stored' group (IRR 1.61; 95% CI 0.63, 4.13) when compared to the 'golden' period. However, we observed a statistically significant association between the 'long-stored' group and an increase in the HAI incidence rate (IRR 3.66; 95% CI 1.22, 10.98). CONCLUSION: Transfusion of leukoreduced RBC units stored for ≥35 days is associated with increased HAI incidence rate in haemodynamically stable, critically ill children.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".