Is Center Specific Implantation Volume a Predictor of Clinical Outcomes with Mechanical Circulatory Support?
Bibliographic record
Abstract
This study examined the relationship between implant center volumes and survival rate for implantation of mechanical circulatory support (MCS) devices. Using the Novacor left ventricular assist system (LVAS) Registry, the study cohort consisted of 1,348 patients with established outcomes from 97 centers stratified by the implant center volume: 1-10 implants (n = 199, 65 centers), 11-25 implants (n = 189, 12 centers), 26-50 implants (n = 445, 13 centers), and more than 51 implants (n = 515, 7 centers). Regression and correlation analyses were performed. Regression analysis found a negative impact on survival for centers performing 1-10 implants, with an odds ratio of 1.73 (95% confidence interval, 1.28-2.34; p < 0.001). Composite results from the first 10 implants of each larger volume center were then compared with the group with 1-10 implants, demonstrating that centers with larger volumes had superior results, even in the early patient experience (61% versus 46% transplanted/weaned, p < 0.001). However, when annualized outcomes (i.e., outcomes by calendar year) were determined for each center, no significant correlation was found between the outcomes and annualized frequency of implantation (R2 = 0.003, n = 422). Although the total number of implants performed at a specific center appeared to impact clinical outcomes, no correlation was found between annualized frequency of implant and clinical outcome.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".