P3160Economic impact assessment of reducing heart failure related hospitalizations in Alberta, Canada by a community-based outpatient heart failure clinic
Bibliographic record
Abstract
Background: Currently, there are 600,000 Canadians living with heart failure (HF) with 50,000 Canadians being diagnosed every year with HF. HF in Canada costs over $2.8 billion per year. There is no cure for HF, and HF patients have long and frequent hospital stays, resulting in high healthcare costs. Community-based heart failure clinics have been proven to reduce hospitalizations, but Alberta has only one. The CHARM (Community Heart Failure Assessment, Rehabilitation and Management) clinic at Advanced Cardiology, Calgary, Alberta is a community based, charity-run, publically funded clinic providing outpatient care, which is physician-directed but RN managed. The aim of this study was to perform an economic impact assessment and the healthcare costs saved by the CHARM clinic during January 2016 to February 2017. Methods: The demographic and clinical data of the patients visiting the CHARM clinic was extracted from patient charts and from the NETCARE system. The average HF-related hospitalization cost per patient in Canada was derived from previously published literature and from inflation data from Statistics Canada. Using the calculated average HF-related cost and multiplying it by the number of hospitalizations saved by the CHARM clinic, we calculated the total healthcare cost saved by the CHARM clinic between Jan 2016 and Feb 2017.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".