Mechanical circulatory support challenges in pediatric and (adult) congenital heart disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: Increased miniaturization of ventricular assist devices (VADs) and new mechanical support strategies (MCS) has increased the use of MCS in the pediatric and congenital heart disease (CHD) population. This comes with the need for care providers specialized in this field to determine optimal patient and device selection, and to improve outcomes and decrease complication rates for new innovative strategies. A review of the published literature in this field is timely and relevant. RECENT FINDINGS: There has been a rapid evolution of using adult designed continuous flow VADS to support children and adults with CHD (ACHD). Patient selection for patients with CHD is complex because of patient size and anatomical diversity and, therefore, makes decision-making complex and unique when compared to general adult practice. Outcomes for children depend on size and diagnosis with neonates with single ventricle physiology being the highest risk candidates. This also holds true for ACHD, in which VAD outcomes in patients with two ventricle physiology are comparable to non-ACHD patients. SUMMARY: In children, there is an increased use of continuous flow devices and a growing experience with outpatient management. Patients with CHD especially when associated with single ventricle physiologies, remain a challenge when it comes to MCS/VAD placement but successful durable VAD implantation with discharge home has been reported.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".