Abstract 14362: Eligibility for Ivabradine in Heart Failure: Representativeness of SHIFT in a Broad Population
Bibliographic record
Abstract
Background: Elevated resting heart rate is a risk factor for adverse outcomes in patients with chronic heart failure (HF). The sinus node inhibitor ivabradine was EMA and FDA approved in 2005 and 2015 respectively, with the SHIFT trial confirming its efficacy and safety. Our objective was to characterize proportions of patients eligible for ivabradine, and the representativeness of the SHIFT clinical trial enrolees as compared to those in the Swedish Heart Failure Registry (SwedeHF). Methods: We examined 26,404 patients with an EF 70 bpm and baseline sinus rhythm. Baseline characteristics and medication use were compared between both groups and change in eligibility over time was reported at 6-months and 1-year in a subset of patients with follow-up registrations of eligibility variables. Results: Overall, 14.2% (n=3741) of patients were SHIFT-type. These patients were more likely to be younger, male, have diabetes, ischemic heart disease, lower EF and more recent onset of HF ( 50% of the target dose compared to 67.3% in non-SHIFT-type patients (p Conclusion: From SwedeHF, ~1 in 10 patients with chronic HF are eligible for ivabradine. These patients have a distinct clinical profile with a large percentage not meeting target beta-blocker dose. SHIFT-type patients who became ineligible over time did so due to lower subsequent heart rates.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".