Clinical Implications of Negatively Adjudicated Heart Failure Events: Data From the VICTORIA Study
Bibliographic record
Abstract
Centralized adjudication of potential endpoints events is standard practice in cardiovascular outcome trials designed for regulatory approval of therapeutics.Adjudication minimizes variability and improves the validity of clinical trials by providing standardized blinded ascertainment of potential endpoints events by clinician reviewers using accepted, prespecified criteria.Published definitions of heart failure (HF) events for event adjudication generally require an inpatient hospital stay that includes a calendar date change as well as specific documentation of at least 1 symptom, 2 physical examination signs, and/or objective evidence such as elevated natriuretic peptides or pulmonary congestion on chest radiographs, and intensified treatment for worsening HF. 1 While this definition is highly specific for HF events, some potential HF events may be negatively adjudicated within a clinical trial due to lack of documentation of one or more of these elements.In some prior studies, this disparity has created differential findings between investigator-identified HF events and adjudicated HF events.[2][3][4] Hence in the Vericiguat Global Study in Subjects with Heart Failure with Reduced Ejection Fraction (VICTORIA) study, we sought to evaluate the clinical impact of negatively adjudicated HF events with a high clinical suspicion for HF.The VICTORIA study was approved by the relevant institutional review committees and all participants
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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 | 0.006 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".