Multidisciplinary outpatient congestive heart failure clinic: impact on hospital admissions and emergency room visits.
Bibliographic record
Abstract
BACKGROUND: Life-saving drugs, such as angiotensin-converting enzyme inhibitors and beta-blockers, are frequently underused and underdosed in patients with heart failure. Specialized clinics have been shown to provide additional benefits. OBJECTIVES: To determine the impact of a multidisciplinary outpatient heart failure clinic on the frequency of cardiovascular readmissions and emergency room (ER) visits, length of inpatient and ER stays, and New York Heart Association (NYHA) class. METHODS: A retrospective chart review study comprising 72 patients who had two or more visits to a heart failure clinic between December 1, 1998, and August 31, 1999, was performed. The number of readmissions and ER visits, and the NYHA class were recorded during the six-month period before and after the first visit to the clinic. RESULTS: Most subjects were in NYHA class III or IV (71% and 21%, respectively), and the mean ejection fraction was 31%. The post- versus preintervention relative risk of readmission was 0.43 (95% CI 0.25 to 0.72). The total number of inpatient days decreased by 54% (95% CI 44% to 62%). The post- versus preintervention relative risk of an ER visit was 0.29 (95% CI 0.19 to 0.45). The number of ER days decreased by 60% (95% CI 41% to 73%). NYHA functional class significantly improved, with most subjects ending the six-month postintervention period in class I or II (33% and 49%, respectively; P=0.001). CONCLUSIONS: This multidisciplinary heart failure clinic significantly decreased the risk of cardiovascular readmissions and subsequent ER visits. It led to improvement in NYHA class and to a decrease in the number of days spent in the hospital and in the ER.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".