Bibliographic record
Abstract
Heart failure represents a complex clinical syndrome characterised by abnormalities of left ventricular function and neurohormonal regulation, exercise intolerance, shortness of breath, fluid retention, and reduced longevity.1 Despite improvements in treatment the prognosis for patients with heart failure remains poor: the risk of death annually is 5%-10% in patients with mild symptoms and 30%-40% in those with advanced disease.2 3 This condition is also associated with major morbidity and healthcare expenditure, being responsible for about 5% of hospital admissions in the United Kingdom.4 ### Box 1: Treatment of heart failure #### Aims #### Treatment modalities Mitral valve surgery Coronary revascularisation Surgical ventricular remodelling procedures Cardiomyoplasty Dual chamber pacing Implantable cardioverter defibrillator treatment Ventricular assist devices Artificial heart Heart transplantation This review deals only with pharmacological treatments in chronic heart failure. Non-pharmacological measures apply to all patients, whereas surgical and device treatments (many still experimental) apply only to specific patient subsets. Patients with clinical symptoms of heart failure but normal or near normal left ventricular systolic function often have impaired left ventricular diastolic function. This heterogeneous group has been generally excluded from heart failure trials. We do not discuss the treatment of diastolic left ventricular dysfunction or acute heart failure syndromes: more comprehensive reviews are available.5 ### Summary points The prognosis for patients with heart failure remains poor Drugs clearly shown to improve survival in patients with heart failure are ACE inhibitors and βblockers These drugs should be used in most patients with heart failure but require …
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.010 |
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".