Neurological Complications and Outcomes in the Berlin Heart EXCOR <sup>®</sup> Pediatric Investigational Device Exemption Trial
Bibliographic record
Abstract
BACKGROUND: The Berlin Heart EXCOR(®) ventricular assist device has been approved for use in the United States as a bridge to heart transplantation in children. We sought to characterize neurological events in children supported with the Berlin Heart EXCOR(®) device. METHODS AND RESULTS: The multicenter prospective cohort consisted of all 204 children implanted with the Berlin Heart EXCOR(®) device at 47 centers in North America between May 2007 and December 2010. There were 73 neurological events in 59 patients, with 29% of the cohort experiencing ≥1 neurological event. Events included 52 strokes in 43 patients (21% of the cohort). The neurological event rate was 0.51 events per 100 patient-days. Many of the neurological events occurred early in the course of support, with 30 events recorded during the first 14 days of support. The mortality rate in participants with at least 1 neurological event was 42% (25 of 59), significantly higher than the 18% mortality rate (26 of 145) for those who did not have a neurological event (P=0.0006). Risk-factor analysis did not identify significant preimplantation predictors of neurological injury. CONCLUSIONS: Of children treated with the Berlin Heart EXCOR(®) device as a bridge to transplant, 29% experienced at least 1 neurological event. The majority of neurological events were ischemic strokes, and many of those occurred early in the course of support. Neurological injury was the leading cause of death after implantation of the Berlin Heart EXCOR(®) device. Risk stratification for stroke or neurological injury is not possible based on baseline preimplantation characteristics. CLINICAL TRIAL REGISTRATION URL: www.clinicaltrials.gov. Unique Identifier: NCT00583661.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".