TACO‐BEL‐3: a feasibility study and a retrospective audit of diuretics for patients receiving blood transfusion at ten hospitals
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Transfusion-associated circulatory overload (TACO) is the leading cause of transfusion-related morbidity and mortality. A recently completed pilot trial randomized patients to pre-transfusion furosemide versus placebo but had a slower than expected enrollment rate. We sought to determine whether the lack of recruitment was due to a paucity of eligible patients or excessively restrictive eligibility criteria. MATERIALS AND METHODS: At 10 sites, eligible patients were retrospectively identified by first screening blood bank databases over one month for all transfusion episodes meeting trial inclusion criteria, defined as non-surgical patients receiving single RBC unit transfusions. The age threshold was decreased from 65 to 50 years. The first 10 patients meeting inclusion criteria then underwent detailed chart review for the exclusion criteria. The incidence of TACO and furosemide use was also recorded. RESULTS: At the 10 sites, 11 969 red cell units were transfused over 1 month and 1356 met the inclusion criteria. Of the 100 charts reviewed, 60 (60%) had no exclusion criteria. Active bleeding was the most common reason for ineligibility. There were 813 eligible transfusion episodes. Of the eligible patients, 17 (28·3%) had evidence of congestive heart failure, and furosemide was prescribed in 24 (40%). Despite the use of a lower age threshold, three cases of TACO were detected with an incidence of 3%. CONCLUSION: A large number of transfusion episodes met eligibility criteria. With a 3% incidence of TACO, 50% decrease through the use pre-transfusion furosemide and a target consent rate of 30%, a definitive trial of approximately 3000 patients could be completed within 1 year.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".