Can furosemide prevent transfusion‐associated circulatory overload? Results of a pilot, double‐blind, randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Transfusion-associated circulatory overload (TACO) is a leading cause of transfusion-attributable morbidity. It is unclear whether diuretics are safe and effective in preventing this reaction. MATERIALS AND METHODS: In a pilot controlled feasibility trial, inpatients 65 years or older ordered a single unit of red blood cells were randomized to pre-transfusion furosemide 20 mg or placebo intravenously. Primary outcome was the ability to enroll 80 patients within a 2-month time period. Secondary feasibility outcomes included proportion of RBC transfusions meeting eligibility criteria, proportion of eligible patients enrolled, and compliance to study protocol. Clinical outcomes included the incidence of TACO and associated complications. RESULTS: Nine months of enrollment were required for 80 patients to complete the study, due primarily to fewer transfusions than expected meeting eligibility criteria and lower than anticipated consent rates. Protocol compliance was below target due to missing chart documentation of patient fluid balance, and transfusion infusion time. Blinding was maintained throughout the study and treatment arms were well-balanced. A single case of TACO occurred in each arm, for an overall incidence of 2.5%. No differences in peri-transfusion vital signs, B-natriuretic peptide, or signs of furosemide toxicity were observed. CONCLUSION: The study protocol was not feasible as designed, primarily due to challenges in patient enrollment. Modifications to trial design to improve feasibility in future studies have been identified.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".