Development of an Interprofessional Pediatric Ventricular Assist Device Support Team
Bibliographic record
Abstract
Caring for pediatric patients with a ventricular assist device (VAD) requires a collaborative approach from an interprofessional team to ensure maximum patient safety and optimal outcomes. Initiating a VAD program is challenging, due to the complex medical and technical nature of this device and associated learning needs. At our institution, the development of the interprofessional VAD support team was established in four phases. Initial Education, Core Team Formation, Expansion, and Evaluation. A "core VAD team" was created after the initial education at an established VAD center. In a third step, all efforts were directed toward increasing the number of health care professionals caring for the VAD patients in the Cardiac Critical Care setting and on the Cardiac ward. The last phase consists of ongoing evaluation. Several key areas imperative to the care of a patient on a VAD were identified and further elaborated. The complex care of a patient on a VAD needs a specialized team approach to cover all patient care needs. Ongoing interprofessional education continues to improve competency of care. Continuity of care was assured on all levels of service to ensure the best possible outcomes.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".