Bibliographic record
Abstract
Co-morbidities play a central and increasing role in patients with heart failure (HF) due to aging of these subjects, 1,2 and have a major impact on the clinical course of HF patients.Some of the most promising new drugs for HF may actually come from their treatment.3,4 Diabetes A position statement from the Heart Failure AssociationUp to 30-40% of patients with HF have type 2 diabetes mellitus.Diabetes worsens cardiac function and patient outcomes.Different antidiabetic drugs have different effects on the clinical course of patients with HF. 5 -9 These and other aspects make particularly worthwhile and welcome the Heart Failure Association position statement about diabetes and HF, 10 which provides an in-depth summary and discussion of all major aspects regarding the interaction between diabetes and HF and the effects of treatment on the clinical course of these two conditions.10 Hyperkalaemia Beusekamp et al. 20 assessed the interaction between serum potassium levels and the dose of angiotensin-converting enzyme
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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.213 | 0.088 |
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".