Bibliographic record
Abstract
PURPOSE OF REVIEW: Heart transplantation remains the treatment of choice for patients with advanced heart failure. We review the current definition of optimal therapy, prediction of prognosis and revisit contraindications for transplant. RECENT FINDINGS: Clinical trials of eplerenone and ivabradine were associated with improved prognosis, whereas others (nesiritide) were disappointing. Advances in cardiac resynchronization therapy and ventricular assist devices (VAD) have resulted in an expansion of their indications. Advances in catheter ablation for ventricular tachycardia have made this an uncommon indication for heart transplantation. Surgical ventricular reconstruction and mitral valve intervention have not resulted in survival benefit. Bypass surgery was associated with a lower mortality from cardiovascular causes. Prognostic risk scores have been developed in heart failure patients; however, ongoing refinements are needed. Selected patients with diabetes, HIV and pretransplant malignancy, now have favourable outcomes after heart transplantation. VAD as bridge to candidacy is an option in heart failure patients with 'fixed' pulmonary hypertension. Alternate listing strategies have also been studied to provide high-risk patients with an opportunity for heart transplantation. SUMMARY: Heart failure patients should be on current optimal medical and device therapy with a poor prognosis before consideration for heart transplantation. An individualized approach to heart transplantation assessment is recommended.
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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) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".