Potential gaps in congestive heart failure management in a rural hospital.
Bibliographic record
Abstract
INTRODUCTION: Congestive heart failure (CHF) is increasingly recognized as an important cause of morbidity and mortality. Previous studies in urban settings have shown that patients frequently are not receiving recommended therapy. There is a paucity of studies that have evaluated CHF management in a rural setting. We therefore reviewed hospital and outpatient care in this setting as an initial step toward improving CHF care. METHOD: A retrospective chart review was used to examine the care of all 34 patients hospitalized for CHF from 2000-2001 in a small rural hospital, to assess the need for improved CHF management. RESULTS: The median age of the patients was 78 yr, and a number of them had many co-morbid cardiovascular risks. Similar to other studies, only 23% of patients were prescribed recommended doses of angiotensin-converting enzyme (ACE) inhibitors. Use of beta-blockers was far below expected rates. Although there was follow-up care for nearly all patients (97%), few patients had echocardiography performed (38%) or had their medications altered in the outpatient setting. CONCLUSION: There is a need for improved management of CHF in the rural setting. Approaches to improving CHF care should use the continuity of care advantage provided by primary care physicians to optimize outpatient medical treatment regimens and improve access to diagnostic services such as echocardiography.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".