Minimally invasive ventricular assist device implantation
Bibliographic record
Abstract
Durable mechanical circulatory support (MCS) systems are established therapy option in patients with end-stage heart failure, with increasing importance during the last years due to donor organ shortage. Left ventricular assist devices (LVADs) are traditionally implanted through median sternotomy (MS). However, improvement in the pump designs during the last years led to evolvement of new surgical approaches that aim to reduce the invasiveness of the procedure. Numerous reports and studies have shown the viability and possible advantages of less-invasive approach compared to the sternotomy approach. The less invasive implant strategies for LVADs, while vague in definition, are characterized by minimizing surgical trauma and if possible, cardio-pulmonary bypass related complications. Usually it involves minimizing or completely avoiding sternal trauma, avoiding heart luxation while simultaneously leaving the major part of pericardium intact. There is no consensus between the centers regarding the ideal approach for LVAD implantation. Some centers, like our center, perform by default VAD implantation using less invasive approach in almost all patients and some centers use only sternotomy approach. The aim of this review article is to shed light on the currently available less invasive options of LVAD implantation, with particular focus on the centrifugal pumps, and their possible advantages compared to traditional sternotomy approach.
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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) | 0.001 | 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) | 0.004 | 0.001 |
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".