August 2016 at a Glance: The New Esc Guidelines, and Pathophysiology, Epidemiology and Prognosis of Heart Failure
Bibliographic record
Abstract
ESC) guidelinesThis issue has our new ESC guidelines for the diagnosis and treatment of heart failure (HF). 1 Compared with the previous 2012 guidelines, major changes regard the introduction of the new category of patients with mid-range left ventricular ejection fraction (EF), a new algorithm for the diagnosis of HF, the inclusion of new exams for patients' evaluation, recommendations to prevent or delay the development of overt HF, including antidiabetic treatment with empaglifozin, a new algorithm for HF treatment, with the replacement of angiotensin-converting enzyme inhibitors or angiotensin receptor blockers with an angiotensin receptor neprilysin inhibitor in the patients still symptomatic with a low EF, updated indications to cardiac resynchronization therapy, new evidence regarding treatment of comorbidities and more detailed indications for mechanical circulatory support. 1 Treatment of HF with preserved EF (HFpEF) and treatment of acute HF are updated and summarized with new algorithms but, unfortunately, no major change is present, as no new drugs have reached evidence for treatment in these conditions.2,3 A last paragraph with our gaps in evidence and a table with the messages about what to do or not to do in HF nicely summarize both future developments and current evidence.
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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.023 | 0.016 |
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".