Contemporary Considerations for the Use of Digoxin for Heart Failure in Older Patients
Bibliographic record
Abstract
Background: Digoxin has been used in older people for over 200 years to treat heart failure. Research over the past 15 years has caused the place of digoxin in therapy to evolve. This review was conducted in order to describe the role of digoxin in the care of older patients with heart failure. Methods: This review was conducted by systematically searching the literature using MEDLINE via Ovid, Cochrane Library, Pub Med and EMBASE, with the search terms “heart failure” and “digoxin.” Studies published after publication of the Digitalis Investigation Group (DIG) trial (conducted from February 1997 to October 2010) were selected for possible inclusion in the review. Results: The majority of data regarding the use of digoxin for heart failure in older people originates from the DIG trial and the various post-hoc analyses of this dataset. When considered in unison with evidence for other heart failure therapies (e.g., angiotensin-converting enzyme inhibitors), the place of digoxin is clear, in that it should be used for patients in sinus rhythm who are symptomatic despite therapy with first-line agents or for those with concomitant atrial fibrillation whose heart rate is not well controlled by, or cannot tolerate, beta-adrenergic blockers. There are various safety and monitoring parameters that should be considered in older people when using this drug. Conclusions: Digoxin is a drug that still demonstrates value for heart failure in older patients when used appropriately, and after first-line agents have been maximized.
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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 | 0.017 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".