Late-Breaking Science Abstracts and Featured Science Abstracts From the American Heart Association's Scientific Sessions 2022 and Late-Breaking Abstracts in Resuscitation Science From the Resuscitation Science Symposium 2022
Bibliographic record
Abstract
Background: Data from prior studies have suggested potential benefits of torsemide over furosemide in patients with heart failure (HF).However, without an adequately powered clinical outcome study, there is insufficient evidence to recommend torsemide over furosemide.Methods: Using an open-label, pragmatic design, we randomly assigned patients hospitalized with HF (regardless of ejection fraction [EF]) to a loop diuretic strategy of torsemide or furosemide at 61 centers in the United States (TRANSFORM-HF Trial; ClinicalTrials.govnumber NCT03296813).The primary outcome was all-cause mortality assessed in a time-to-event analysis; the trial was event-driven targeting 721 primary events (Figure ).Results: Recruitment began in June 2018 and following a routine DSMB meeting in February 2022, the DSMB recommended stopping recruitment because the sample size was sufficient to answer the primary research question with statistical validity.Recruitment ended March 2022 with 2,859 randomized participants.The median age was 65 years (IQR: 56-75), 37% were women, 34% were Black, and 25% had an EF ≥50%.Database lock occurred August 12, 2022 and the primary results will be available for presentation at the AHA 2022 Scientific Session.Conclusions: We will present the primary results of this large, open-label, pragmatic, multi-center trial of loop diuretics in patients with HF to demonstrate whether torsemide improves clinical and patient-reported outcomes compared with furosemide.(Funded by the National Heart, Lung and Blood Institute)
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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 | 0.052 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.283 | 0.082 |
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".