Decisional Needs and Patient Treatment Preferences for Heart Failure Medications: A Scoping Review
Bibliographic record
Abstract
Background: Pharmacologic management of heart failure with reduced ejection fraction (HFrEF) involves several medications. Decision aids informed by patient decisional needs and treatment preferences could assist in making HFrEF medication choices; however, these are largely unknown. Methods: We searched MEDLINE, Embase, and the Cumulative Index to Nursing and Allied Health Literature (CINAHL), without language restriction, for qualitative, quantitative, and mixed-method studies that included patients with HFrEF or clinicians providing HFrEF care, and reported data on decisional needs or treatment preferences applicable to HFrEF medications. We classified decisional needs using a modified version of the Ottawa Decision Support Framework (ODSF). Results: From 3996 records, we included 16 reports describing 13 studies (n = 854). No study explicitly assessed ODSF decisional needs; however, 11 studies reported ODSF-classifiable data. Patients commonly reported having inadequate knowledge or information, and difficult decisional roles. No study systematically assessed treatment preferences, but 6 studies reported on attribute preferences. Reducing mortality and improving symptoms frequently were ranked as being important, whereas cost importance rankings varied, and adverse events generally were ranked as being less important. Conclusion: This scoping review identified key decisional needs regarding HFrEF medications, notably inadequate knowledge or information, and difficult decisional roles, which can readily be addressed by decision aids. Future studies should systematically explore the full scope of ODSF-based decisional needs in patients with HFrEF, along with relative preferences among treatment attributes to further inform development of individualized decision aids.
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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 | 0.025 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".