Reply to ‘Vericiguat in Heart Failure with Reduced Ejection Fraction: The Right Choice Above All Else? The Answer May Lie in Time’
Bibliographic record
Abstract
We appreciate the interest shown by Dr. Wang and colleagues regarding our recent publication.1 They raise three issues germane to our results: we welcome the opportunity to respond. First, with respect to the potential confounding effect of concurrent use of sodium–glucose cotransporter 2 inhibitors, it should be noted that we completed VICTORIA enrolment on December 2018, prior to the current widespread use of these agents. Hence, only 3.6% of the echo population received this therapy; there was no significant difference in their use between treatment groups. Therefore, we contend this therapy was unlikely to have influenced our results. Second, by the time the 8-month follow-up period had elapsed, 80% of the heart failure hospitalizations had already occurred in our substudy cohort, which was also consistent with the overall trial population. Moreover, in parallel with the decline in left ventricular end-systolic volume index by at least 15% at 8 months, the composite outcome of heart failure hospitalization or cardiovascular death also declined from 33.2 to 12.2 events per 100 patient-years (adjusted hazard ratio 2.46, 95% confidence interval 1.41–4.31; p = 0.002) in the substudy population. Whereas we agree that ventricular remodelling may continue for many months, in the SOLVD (Studies of Left Ventricular Dysfunction) trials substantial differences in ventricular volumes between enalapril- and placebo-treated patients were already evident prior to 6 months suggesting our time frame of observations was more than adequate to capture meaningful differences between treatment groups.2 Third, de facto the population was by definition at lower risk than the overall VICTORIA population given the requirement to survive to 8 months for the follow-up echocardiographic assessment. The most prominent modulator of clinical outcomes (i.e. N-terminal pro-B-type natriuretic peptide) was similar in both treatment groups. We agree further study is required to define the mechanistic basis for vericiguat's efficacy.
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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 | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.037 | 0.047 |
| Insufficient payload (model declined to judge) | 0.012 | 0.012 |
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".