Educational Impact of Targeted Neonatal Echocardiography and Hemodynamics Programs on Neonatal-Perinatal Medicine Fellows
Bibliographic record
Abstract
OBJECTIVE: Targeted neonatal echocardiography (TNE) is a real-time cardiac imaging modality used by a hemodynamics program to aid in diagnosis, treatment, and monitoring of neonatal cardiovascular illness. This study aimed to describe trainees' perspectives on existing hemodynamics education and perceived impacts of TNE and hemodynamics services on their education. STUDY DESIGN: This was a mixed quantitative and qualitative study that surveyed neonatal-perinatal medicine (NPM) fellow trainees in Canada and the United States, at programs both with and without a hemodynamics service. RESULTS: = 0.040). Twenty-five percent of all trainees felt they do not have sufficient hemodynamics training to prepare them for independent practice. Areas of knowledge gaps were identified. Bedside teaching combined with didactic teaching was identified as useful means of teaching. CONCLUSION: Most trainees believed that TNE and a hemodynamics service are valuable educational assets. Thoughtful curriculum design for real-time and consolidation learning, with specific emphasis on content gaps, should be considered. KEY POINTS: · NPM Fellows perceive TNE & Neonatal Hemodynamics service as a valuable educational opportunity.. · Incorporation of TNE/Hemodynamics teaching into NPM curriculum can enrich trainee experience.. · Combining bedside and classroom teaching is key to successful cardiovascular training..
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".