Usefulness of a titration algorithm for de novo users of sacubitril/valsartan in a tertiary centre heart failure clinic
Bibliographic record
Abstract
BACKGROUND: A reduction in the rate of death and hospitalisations in patients with heart failure (HF) with reduced ejection fraction receiving sacubitril/valsartan compared to enalapril was demonstrated in the PARADIGM-HF study. However, tolerability when initiating and optimising sacubitril/valsartan treatment in real clinical practice is unknown. METHODS: We performed a prospective cohort study of clinical and biochemical parameters of the first 100 patients receiving sacubitril/valsartan in a tertiary HF clinic. Patients had titration of the molecule guided by an algorithm developed by pharmacists and cardiologists in the clinic. The objective was to evaluate the proportion of patients reaching the maximal dosage, the time to reach maximal dosage, and the rate of adverse events, as well as the required modification of other HF therapy during the sacubitril/valsartan titration. RESULTS: = 0.0005). CONCLUSIONS: This algorithm is a safe and easy-to-use tool in daily clinical practice for the introduction and titration of sacubitril/valsartan. Almost half of the patients reached the maximal dose, with a tolerability profile in line with the original study.
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.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".