Advanced heart failure in critical patients (INTERMACS 1 and 2 levels): ventricular assist devices or emergency transplantation?
Bibliographic record
Abstract
OBJECTIVE: For patients in advanced heart failure, emergency transplantation or ventricular assist devices (VADs) are possible strategies. The aim of this single-centre, retrospective study was to evaluate early and long-term results for these two strategies. METHODS: From 2005 to 2011, we analysed 49 INTERMACS level 1 and 2 patients, who were divided into the following two groups: group A comprised 26 patients on the waiting list for heart transplantation with urgent conditions; and group B comprised 23 patients who underwent VAD implantation as a bridge to candidacy. RESULTS: In group A, 25 patients underwent transplantation. In group B, 19 patients were supported with left VAD and four with biventricular VAD. Of these 23 patients, 13 underwent transplantation (mean time 279 ± 196 days). The 30 day mortality was 42.3 and 4.3% in group A and B, respectively. Survival at 6 and 12 months was significantly better in group B than in group A (87 vs 53%, P = 0.018 at 6 months; and 77 vs 48%, P = 0.045 at 12 months). CONCLUSION: Improved outcomes may justify the use of mechanical assistance devices as a bridge to candidacy or bridge to transplantation in INTERMACS 1 and 2 patients in order to avoid high-risk transplants. Evaluation of long-term multicentre outcomes is needed to assess future strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".