Ivabradine in Heart Failure
Bibliographic record
Abstract
Background: The sinus node inhibitor ivabradine was approved for patients with heart failure (HF) after the ivabradine and outcomes in chronic HF (SHIFT [Systolic Heart Failure Treatment With the IF Inhibitor Ivabradine Trial]) trial. Our objective was to characterize the proportion of patients with HF eligible for ivabradine and the representativeness of the SHIFT trial enrollees compared with those in the Swedish Heart Failure Registry. Methods and Results: We examined 26 404 patients with clinical HF from the Swedish Heart Failure Registry and divided them into SHIFT type (left ventricular ejection fraction <40%, New York Heart Association class II–IV, sinus rhythm, and heart rate ≥70 beats per minute) and non-SHIFT type. Baseline characteristics and medication use were compared and change in eligibility over time was reported at 6 months and 1 year in a subset of patients. Overall, 14.2% (n=3741) of patients were SHIFT type. These patients were more likely to be younger, men, have diabetes mellitus, ischemic heart disease, lower left ventricular ejection fraction, and more recent onset HF (<6 months; all, P <0.001). Although 88.9% of SHIFT type and 88.5% of non-SHIFT type ( P =0.421) were receiving selected β-blockers, only 58.8% and 67.3% ( P <0.001) were on >50% of target dose. From those patients who had repeated visits within 6 months (n=5420) and 1 year (n=6840), respectively, 10.2% (n=555) and 10.6% (n=724) of SHIFT-type patients became ineligible, 77.3% (n=4188) and 77.3% (n=5287) remained ineligible, and 4.6% (n=252) and 4.9% (n=335) of non-SHIFT–type patients became eligible for initiation of ivabradine. Conclusions: From the Swedish Heart Failure Registry, 14.2% of patients with HF were eligible for ivabradine. These patients more commonly were not receiving target β-blocker dose. Over time, a minority of patients became ineligible and an even smaller minority became eligible.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".