Dynamics of chronic heart failure with a low ejection fraction when dapagliflozin is included in standard therapy
Bibliographic record
Abstract
Objective. The aim of the current research is the dapagliflozin influence on the key parameters that shows the severity of heart failure among patients with chronic heart failure with low ejection fraction. Materials and methods. The research involves 30 mail patients average aged 61,6 ± 11,1. The basis of involving the patients is the following: heart failure with low ejection fraction of any origin and common therapy of chronic heart failure including inhibitors of angiotension converting enzyme/ antagonist of angiotension II receptors, β-blockers, antagonist of mineralocorticoid receptors. The NTproBNP level in blood was defined and there were the transthoracal echocardioscopy with Doppler mapping along with ejection fraction determination according to Simpson, the 6-minute walking test, the questionnaire on health EQ-5D, the Montreal assessment scale of cognitive functions among the heart failure with low ejection fraction patients of the inception cohort and in 6-month period affected by the therapy with SGLT2 inhibitors. Results. The therapy with dapagliflozin has caused the decrease in the NTproBNT level up to 1914,0 ± 500,7 pg/ml, p = 0,001 that 36% less then baseline indexes. There was the increase in ejection fraction of left ventricle by 15,01% comparing with the baseline (35,2 ± 8,6% – baseline, 40,5 ± 10,8% after the SGLT2 inhibitor therapy, p = 0,07). While having 6-minute walking test the significant walking distance increase was found out. It enables us to make the conclusion about the changes of the heart failure functional class according to NYHA from III to II. Analyzing the health status level (according to the questionnaire on health EQ-5D) there was the increase from 56,4 ± 20,4 to 65,8 ± 10,1. According to the Montreal assessment scale there was enhancement of indexes: 24,4 ± 1,6 – baseline, 25,3 ± 1,7 – after the SGLT2 inhibitor therapy, p = 0,34.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".