Congestive heart failure adherence redesign trial: a pilot study
Bibliographic record
Abstract
Objective Heart failure (HF) continues to be a leading cause of hospital admissions, particularly in underserved patients. We hypothesised that providing individualised self-management support to patients and feedback on use of evidence-based HF therapies (EBT) to physicians could lead to improvements in care and decrease hospitalisations. To assess the feasibility of conducting a larger trial testing the efficacy of this dual-level intervention, we conducted the Congestive Heart failure Adherence Redesign Trial Pilot (CHART-P), a proof-of-concept, quasi-experimental, feasibility pilot study. Setting A large tertiary care medical centre in Chicago. Participants Low-income patients ( Interventions Physicians received HF guidelines and periodic individualised feedback on their adherence to EBT. Patients received HF education, support and self-management training for diet and medication adherence by a trained nurse through 11 interactive sessions over a 4-month period. Evaluations were conducted pre-enrolment and 1 month postintervention completion. Outcome measures Feasibility was assessed by the ability to deliver intervention to patients and physicians. Exploratory outcomes included changes in medication and sodium intake for patients and adherence to EBT for physicians. Results Eighty-seven per cent and 82% of patients received >80% of interventions at 1 month and by study completion, respectively. Median sodium intake declined (3.5 vs 2.0 g; p<0.01). There was no statistically significant change in medication adherence based on electronic pill cap monitoring or the Morisky Medication Adherence Scale (MMAS); however, there was a trend towards improved adherence based on MMAS. All physicians received timely intervention. Conclusions This pilot study demonstrated that the protocol was feasible. It provided important insights about the need for intervention and the difficulties in treating patients with a variety of psychosocial problems that undercut their effective care.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".