Pediatric ventricular assist device registries: update and perspectives in the era of miniaturized continuous-flow pumps
Bibliographic record
Abstract
The success of ventricular assist devices (VADs) in the treatment of end-stage heart failure in the adult population has led to industrial innovation in VAD design, focusing on miniaturization and the reduction of complications. A byproduct of these innovations was that newer generation devices could have clinical applications in the pediatric population. Over the last decade, VAD usage in the pediatric population has increased dramatically, and the newer generation continuous flow (CF) devices have begun to supplant the older, pulsatile flow (PF) devices, formerly the sole option for ventricular assist in the pediatric population. However, despite the increase in VAD implants in the pediatric population, patient numbers remain low, and the need to share data between pediatric VAD centers has become that much more important for the continued growth of VAD programs worldwide. The creation of pediatric VAD registries, such as the Pediatric Registry for Mechanical Circulatory Support (PediMACS), the European Registry for Patients with Mechanical Circulatory Support (EUROMACS) and the Advanced Cardiac Therapies Improving Outcomes Network (ACTION) has enabled the collection of aggregate data from VAD centers worldwide, and provides a valuable resource for clinicians and programs, as more and more pediatric heart failure patients are considered candidates for VAD therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".