October 2019 at a Glance: Epidemiology, Prevention, and Modes of Death
Bibliographic record
Abstract
Heart Failure Association consensus meeting report Update on heart failureImportant changes occurred since the publication of the last guidelines on heart failure (HF).Seferovic et al. 1 summarized in a consensus document the major advances that occurred in HF treatment.These include the effects of sodium-glucose co-transporter 2 inhibitors in type 2 diabetes mellitus, MitraClip for functional mitral regurgitation, atrial fibrillation (AF) ablation in HF, tafamidis in cardiac transthyretin amyloidosis, rivaroxaban in HF in sinus rhythm, implantable cardioverter-defibrillators (ICD) in non-ischaemic HF and telemedicine. Epidemiology and prevention Geographic differencesTromp et al. 2 reviewed geographic differences in aetiology, co-morbidities, use of guideline-directed medical therapies, use of devices and outcomes, among HF patients from different geographic areas.They reported also the role of socioeconomic determinants, such as country income level and out-of-pocket costs, for quality of care and clinical outcomes across different countries.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.068 | 0.026 |
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".