Optimal Usage of Sacubitril/Valsartan for the Treatment of Heart Failure: The Importance of Optimizing Heart Failure Care in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) with reduced ejection fraction represents approximately 50% of the 600,000 Canadians currently living with HF and over 90,000 new cases diagnosed each year. The angiotensin receptor neprilysin inhibitor, sacubitril/valsartan, demonstrated superior efficacy in reducing cardiovascular death and HF hospitalization over standard of care therapy. METHODS: The potential magnitude of benefit in Canada with respect to preventing or postponing deaths and reducing hospitalizations resulting from its optimal implementation in patients with HF with an ejection fraction <40% was estimated based on published sources. RESULTS: Of the potentially eligible 225,562 patients, this would amount to the prevention of 4699 cardiovascular deaths and first HF hospitalizations, 3698 thirty-day HF readmissions, and 2820 deaths due to all-cause mortality. The number of patients receiving sacubitril/valsartan nationally in 2018 was 27,267. This represents approximately 12% of the calculated eligible population for this therapy in Canada. CONCLUSIONS: The findings from this analysis suggest that a substantial number of deaths, hospitalizations, and HF readmissions could potentially be avoided by optimal usage of sacubitril/valsartan therapy in Canada. This emphasizes the importance of rapidly and appropriately implementing evidence-based medications into routine clinical practice, to achieve the best possible outcomes for our patients with HF and to reduce the high burden and cost of HF in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it