Factors related to time to admission to specialized multidisciplinary clinics in patients with congestive heart failure
Bibliographic record
Abstract
BACKGROUND: Congestive heart failure (CHF) is a common cause of hospitalization and has a poor prognosis. Specialized multidisciplinary clinics are effective in the management of CHF. OBJECTIVES: To measure time of admission to the specialized clinics and explore factors related to the time of admission to these clinics. METHODS: Patients who were newly admitted to one of six CHF multidisciplinary clinics in the province of Quebec were enrolled in the study. Data were collected from the common clinical database used at these clinics as well as from questionnaires administered to the patients. RESULTS: A total of 531 patients with a mean age of 65.9 years were enrolled. Only 26% were women. The median duration of disease before admission to the CHF clinic was 1.2 years. The majority of patients (62%) were referred by a cardiologist or an internist, while 24% were referred by other specialists, and 14% by general practitioners. One-fifth of patients did not have regular follow-up for their CHF before being admitted to the clinic. Factors associated with shorter disease duration at admission to the clinic were referral by a specialist, not having regular medical follow-up for CHF, having a higher income and having visited the emergency room for CHF. CONCLUSION: There may be a need to improve dissemination of information regarding availability and benefits of CHF clinics and criteria for referral.
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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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".