Restrictive Transfusion Strategy Is More Effective in Massive Burns: Results of the TRIBE Multicenter Prospective Randomized Trial
Bibliographic record
Abstract
OBJECTIVES: Studies suggest that a restrictive transfusion strategy is safe in burns, yet the efficacy of a restrictive transfusion policy in massive burn injury is uncertain. Our objective: compare outcomes between massive burn (≥60% total body surface area (TBSA) burn) and major (20-59% TBSA) burn using a restrictive or a liberal blood transfusion strategy. METHODS: Patients with burns ≥20% were block randomized by age and TBSA to a restrictive (transfuse hemoglobin <7 g/dL) or liberal (transfuse hemoglobin <10 g/dL) strategy throughout hospitalization. Data collected included demographics, infections, transfusions, and outcomes. RESULTS: Three hundred and forty-five patients received 7,054 units blood, 2,886 in massive and 4,168 in restrictive. Patients were similar in age, TBSA, and inhalation injury. The restrictive group received less blood (45.57 ± 47.63 vs. 77.16 ± 55.0, p < 0.03 massive; 11.0 ± 16.70 vs. 16.78 ± 17.39, p < 0.001) major). In massive burn, the restrictive group had fewer ventilator days (p < 0.05). Median ICU days and LOS were lower in the restrictive group; wound healing, mortality, and infection did not differ. No significant outcome differences occurred in the major (20-59%) group (p > 0.05). CONCLUSIONS: A restrictive transfusion strategy may be beneficial in massive burns in reducing ventilator days, ICU days and blood utilization, but does not decrease infection, mortality, hospital LOS or wound healing.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".