Outcomes following restrictive or liberal red blood cell transfusion in patients with lower gastrointestinal bleeding
Bibliographic record
Abstract
BACKGROUND: Restrictive red blood cell (RBC) transfusion reduces mortality and rebleeding after upper gastrointestinal bleeding (UGIB). However, there is no evidence to guide transfusion strategies in lower gastrointestinal bleeding (LGIB). AIM: To assess the association between RBC transfusion strategies and outcomes in patients with LGIB METHODS: This was a post hoc analysis of the UK National Comparative Audit of LGIB and the Use of Blood. The relationships between liberal RBC transfusion and clinical outcomes of rebleeding, mortality and a composite outcome for safe discharge were examined. Transfusion strategy was dichotomised and defined as "liberal" when transfusion was administered for haemoglobin (Hb) ≥80 g/L (or ≥90 g/L in patients with acute coronary syndrome) or major haemorrhage, and "restrictive" otherwise. Multivariable logistic regression models were used to assess the independent association between liberal RBC transfusion and outcomes. RESULTS: Of 2528 consecutive patients enrolled from 143 hospitals in the original study, 666 (26.3%) received RBC transfusion (mean age 73.3 ± 16 years, 49% female, initial mean haemoglobin 90 ± 24 g/L, 2.3% had haemodynamic instability). The rebleeding rate in transfused patients was 42.3%. After adjusting for potential confounders, there was no difference between liberal and restrictive RBC transfusion strategies for the odds of rebleeding (OR 0.89, 95% CI 0.6-1.22), in-hospital mortality (OR 0.54, 95% CI 0.3-1.1) or of achieving the composite outcome (OR 0.72, 95% CI 0.5-1.1). CONCLUSION: Although these results could be due to residual confounding, they provide an important foundation for the design of randomised trials to evaluate transfusion strategies for LGIB.
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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 | 0.002 | 0.010 |
| 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) | 0.001 | 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".