Bibliographic record
Abstract
Prediction of heart failureBhambhani et al. 1 pooled data from four community-based longitudinal cohorts, including 28 820 subjects free of heart failure (HF) at entry and followed for 12 years, to identify predictive variables for the development of HF with mid-range ejection fraction (HFmrEF), compared with HF with reduced (HFrEF) or preserved ejection fraction (HFpEF).Clinical predictors of HFmrEF included age, male sex, systolic blood pressure, diabetes mellitus, prior myocardial infarction, natriuretic peptides, cystatin-C, and high-sensitivity troponin.Natriuretic peptides were also the strongest predictors of HFrEF.All-cause mortality following the onset of HFmrEF was similar to that of HFrEF and worse than that of HFpEF. 1 Similarities between HFmrEF and HFrEF are therefore confirmed with respect to their risk factors, whereas the outcome of HFmrEF patients has been often found as better compared to that of HFrEF patients.2 Delles et al. 3 studied by nuclear magnetic resonance-based metabolomics two cohorts of subjects at risk of cardiovascular events and identified, among 80 metabolites, phenylalanine as a predictor of incident HF hospitalizations.Although its additive predictive value was modest, it remained significant after adjustment for baseline variables and natriuretic peptide plasma levels. Cardiac amyloidosisSiegismund et al. 15 analysed endomyocardial biopsies from 54 consecutive patients with amyloidosis.Patients with cardiac amyloid light-chain (AL) amyloidosis had a poorer prognosis than those
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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.065 | 0.029 |
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".