Abstract 9578: Single-Item Self-Report Medication Adherence Question Predicts Hospitalization and Death in Patients with Heart Failure
Bibliographic record
Abstract
Background: Sub-optimal adherence to heart failure (HF) medications contributes to worse outcomes. Having a simple means of identifying sub-optimal adherence could help identify at-risk patients for interventions. Objective: To determine whether a single-item self-report medication adherence question predicts hospitalization and death in patients with HF. Methods: We performed an observational study in 597 patients with HF (male 52%, age 61±13 years, NYHA III/IV 31%) within a 4-site randomized trial. Patient demographic and clinical variables were collected at baseline by interview and medical record review. Self-report medication adherence was assessed at baseline using a single item question: “Over the past 7 days, how many times did you miss a dose of any of your heart medication?” Patients who reported no missing dose were defined as adherent; those missing ≥ 1 dose were non-adherent. The primary outcome was combined all-cause hospitalization or death over 1 year; secondary endpoint was HF hospitalization. Outcomes were assessed based on blinded chart reviews and HF outcomes were determined by a blinded adjudication committee. We used negative binomial regression to examine the relationship between medication adherence and incidence of hospitalization and death, adjusting for site, demographic (age, gender, ethnicity, socioeconomic status) and clinical (HF symptoms, systolic dysfunction, β-blocker use, hypertension, and history of CVD) factors. Results: Seventy-three percent of patients reported perfect adherence to their heart medicine at baseline. Patients with perfect medication adherence had a lower rate of events (0.71 events / year) compared with those with any non-adherence (0.90 event / year). After adjusting for site, adherent patients had an incidence rate ratio (IRR) of 0.79 (95% CI: 0.67-0.92) for all-cause hospitalization or death, and 0.79 (95% CI: 0.75-0.83) for HF hospitalization. When adding demographic and clinical factors to the model, the adjusted IRR for all-cause hospitalization and death = 0.77 (95% CI: 0.68-0.86) and for HF hospitalization = 0.83 (95% CI: 0.65-1.06). Conclusion: A single question on medication adherence at baseline predicts hospitalization and death over 1 year in patients with HF.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".