Pediatric Critical Care Neurologists in the United States and Canada: A Survey of Clinical Practice Experience
Bibliographic record
Abstract
ObjectiveTo describe the characteristics of pediatric intensive care neurologists and their practice in the United States and Canada.MethodsWe performed a survey-based study of child neurologists who self-identify as ‘intensive care neurologists’. The survey included questions about demographics, training, pediatric neurocritical care service and job structure, teaching, academics, challenges, and views on the future of pediatric neurocritical care.ResultsWe analyzed 55 surveys. Most respondents were 31-50 years of age with ≤10 years of practice experience. Fifty-four percent identified as female. Most completed subspecialty training after child neurology residency. The majority practice at highly resourced centers with >45 intensive care unit beds. Respondents cover a variety of inpatient (critical and noncritical care) services, at times simultaneously, for a median of 19.5 weeks/y and work >70 hours/wk when on service for pediatric neurocritical care. The top 3 challenges reported were competing demands for time, excess volume, and communication with critical care medicine. Top priorities for the “ideal pediatric neurocritical care service” were attendings with training in pediatric neurocritical care or a related field and joint rounding with critical care medicine.ConclusionWe report a survey-based analysis of the demographics and scope of practice of pediatric critical care neurologists. We highlight challenges faced and provide a framework for the further development of this rapidly growing field.
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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 | 0.010 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".