October 2023 at a Glance: From Prevention to Diagnosis, Prognosis and Treatment of Acute Decompensation and Comorbidities
Bibliographic record
Abstract
PreventionPrevention of heart failure (HF) has a key role in our health care.1,2 Multivariable prediction models are frequently used to estimate the risk of incident HF.A systematic Bayesian meta-analysis, including 36 studies and 59 models for the prediction of HF, showed their predictive accuracy.However, 77% of model results were at high risk of bias, certainty of evidence was low, and no model had a clinical impact assessment.3 Monzo et al. 4 investigated the association of aldosterone concentrations with left ventricular (LV) remodelling after acute myocardial infarction (MI) in patients successfully treated by primary percutaneous coronary angioplasty for a first acute ST-elevation MI.LV volumes were measured within 4 days after acute MI using cardiac magnetic resonance and transthoracic echocardiography, 6 months later and, in a subset of cases, 3-9 years later.Aldosterone concentrations were associated with LV remodelling at 6 months, even in patients with an initial LV ejection fraction (LVEF) >40%, but not in the long term follow-up.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.174 | 0.053 |
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".