Key Articles and Guidelines in the Management of Heart Failure: 2018 Update
Bibliographic record
Abstract
Heart failure is one of the leading causes of hospitalizations in the United States, with >1 million admissions yearly and a 25% risk of readmissions within 1 month. In order to assist clinicians, we provide an update of the heart failure bibliography that was published in Pharmacotherapy in 2008, which followed the original bibliography published in 2004. A significant number of clinical trials and observational studies have been conducted since the early 1980s to guide management of heart failure patients. Major advances have occurred in the past 10 years, and our understanding of the diagnosis, prevention, and management of heart failure has evolved substantially during this time period. Specific areas of this review include heart failure risk factors, management of comorbid conditions, acute heart failure management, chronic heart failure management, advanced heart failure, device therapy, lifestyle modification, and medication and therapy management, including medication adherence. Key consensus guidelines and statements are also included. This bibliography of key heart failure papers aims to provide clinicians and their trainees with a valuable clinical reference resource and teaching tool that may be used to optimize the care of patients with heart failure.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".