August 2017 at a Glance: Tailored Treatment, Acute Heart Failure and Cardiac Resynchronization
Bibliographic record
Abstract
Tailored treatment: the case of mineralocorticoid receptor antagonistsFerreira et al. review treatment with mineralocorticoid receptor antagonists (MRAs) in patients with heart failure (HF). 1 Randomized controlled trials have shown that these drugs are effective in most of the patients with HF and reduced ejection fraction (HFrEF), including those with mild to moderate renal dysfunction or with hypokalaemia or mild hyperkalaemia. 1 New MRAs and/or drugs preventing hyperkalaemia may further increase the indication to MRAs. 2 -4 Different from HFrEF, better patient selection seems warranted in those showing HF with preserved ejection fraction (HFpEF).The trial conducted in these patients, the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT), showed marked geographical differences.5,6 Spironolactone had favorable effects in patients in the prespecified randomization stratum with high NT-proBNP levels at baseline, and these patients with HFpEF and high BNP levels may be considered as potential targets for treatment with MRAs.1,7 Further indications to MRAs may come from studies in patients with acute HF at high risk of events and, on the opposite side of the spectrum, from studies in asymptomatic patients with increased markers of fibrosis, cardiac damage, or inflammation.1 admitted for acute coronary syndrome.16 The best model was based on seven variables, each with a specific score: female sex 25,
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".