Fellowship Training in the Emerging Fields of Fetal-Neonatal Neurology and Neonatal Neurocritical Care
Bibliographic record
Abstract
BACKGROUND: Neonatal neurocritical care is a growing and rapidly evolving medical subspecialty, with increasing numbers of dedicated multidisciplinary clinical, educational, and research programs established at academic institutions. The growth of these programs has provided trainees in neurology, neonatology, and pediatrics with increased exposure to the field, sparking interest in dedicated fellowship training in fetal-neonatal neurology. OBJECTIVES: To meet this rising demand, increasing numbers of training programs are being established to provide trainees with the requisite knowledge and skills to independently deliver care for infants with neurological injury or impairment from the fetal care center and neonatal intensive care unit to the outpatient clinic. This article provides an initial framework for standardization of training across these programs. RESULTS: Recommendations include goals and objectives for training in the field; core areas where clinical competency must be demonstrated; training activities and neuroimaging and neurodiagnostic modalities which require proficiency; and programmatic requirements necessary to support a comprehensive and well-rounded training program. CONCLUSIONS: With consistent implementation, the proposed model has the potential to establish recognized standards of professional excellence for training in the field, provide a pathway toward Accreditation Council for Graduate Medical Education certification for program graduates, and lead to continued improvements in medical and neurological care provided to patients in the neonatal intensive care unit.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".