Transfusion-related acute lung injury in a paediatric intensive care unit of Pakistan
Bibliographic record
Abstract
Background: Transfusion-Related Acute Lung Injury (TRALI) is a major cause of transfusionrelated morbidity and mortality in the intensive care unit setting. There is a paucity of such data from Pakistan. The purpose of this study is to assess the incidence and outcome of TRALI in critically ill children admitted in a pediatric intensive care unit (PICU) of Pakistan.Methods: This is a retrospective cohort study of all critically ill or injured children who developed TRALI or "possible" TRALI after blood transfusion based on Canadian Conference Consensus criteria in a closed multidisciplinary-cardiothoracic PICU from January 2012 to June 2016. The demographic, pertinent clinical data, transfusion-related variables and outcome of all cases of TRALI were recorded.Results: Of total 2975 admissions in the PICU during study period, 35.8% (1066) received 5124 blood components. Eleven cases developed TRALI in our cohort. The incidence of TRALI was 1.03% per patient transfused and 0.19% (19/100,000 per blood product transfused). Median age was 8 (range 1-14) yr., 70 % (n=8) were male. Mean PRISM-III score was 16.3±6.7. Mean time interval for onset of TRALI was 2.73±1.67 hr. The postoperative cardiac surgical and hematology-oncology patients were most common categories (63.6%). Plasma and platelets were the most commomly identified trigger of TRALI. The case-specific mortality was 63.6% and the overall mortality was 10.7% (p<0.0001).Conclusions: The incidence of TRALI in critically ill children is low, but is associated with high mortality. Critically ill children with high PRISM-III score, postoperative cardiac surgical and hematology-oncology patients are often affected by TRALI.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".