Major Predictors of Incidence of Congestive Heart Failure and the Responsive Character of Enteral Nutrition: Meta-Analysis
Bibliographic record
Abstract
Background: Statistical data from the USA estimate that 5.7 million Americans over 20 years of age have congestive heart failure (CHF) and this number is expected to increase approximately 46.0% between 2012 and 2030. In Brazil, there are no epidemiological studies involving the incidence of heart failure; however, according to other countries, it can be estimated that up to 6.4 million Brazilians suffer from this syndrome. Randomized and controlled clinical studies on the efficacy of enteral nutrition (EN) in patients with CHF are lacking. The aim of the present study was to perform a systematic review of the main predictors of CHF that promote EN, as well as to find if the literary findings were conclusive in the efficacy of EN for treatment and prophylaxis of CHF. Methods: A total of 105 papers were submitted to the eligibility analysis, after which 28 studies were selected, following the rules of the systematic review - PRISMA. The search strategy was followed in MEDLINE/Pubmed, Web of Science, ScienceDirect Journals (Elsevier), Scopus (Elsevier) and ONEFile (Gale), with the following steps: search for mesh terms and use of bouleanos "and " between terms and "or" between historical findings. Results: The present study listed the major predictors of CHF with indication for EN. After testing the normality of each group of variables of causes of decompensation in CHF, it was analyzed that all the variables did not present normal distribution, with P < 0.10. Thus, a non-parametric Kruskal-Wallis analysis was performed, obtaining P > 0.05 in all analyses, that is, in all groups of causes of decompensation in CHF, there was no statistical difference in each group studied. Conclusion: There is still no known influence of the efficacy of EN on increasing survival and reducing the morbidity of patients with CHF because there are few clinical trials that have evaluated this question; however, EN is very indicated in the attempt to mitigate the weight loss in these patients. Cardiol Res. 2018;9(5):273-278 doi: https://doi.org/10.14740/cr746w
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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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.051 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".