The State of Inpatient Child Neurology
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Inpatient child neurology programs provide essential services for children. We sought to understand the current structure and challenges of inpatient pediatric neurologic care delivery in academic programs in North America. METHODS: We identified a single child neurologist from 39 of the first 40 programs on the 2019-2020 US News and World Report ranking and 3 large Canadian programs to be invited to participate in an inpatient focused survey. In October 2020, these 42 child neurologists were invited to complete an anonymous on-line survey including 37 questions about the structure, workload, and challenges of their inpatient program. Data were analyzed descriptively. RESULTS: We received responses from 30/42 (71%) invited child neurologists from unique programs. Most (22/30, 73%) were Child Neurology Program Directors, Inpatient Directors, and/or Division Chiefs. Two-thirds (20/30, 67%) reported a total of 2-4 inpatient services. Two-thirds (20/30, 67%) reported a primary neurology admitting service. Nearly two-thirds (19/30, 63%) reported a separate intensive care unit service, and approximately one-third (11/30, 37%) reported a separate stroke/vascular service. Half of the respondents (15/30, 50%) reported some attendings whose primary clinical effort is in the inpatient setting. Over half (17/30, 57%) reported having trainees interested in inpatient-focused careers. Approximately half (16/30, 53%) reported a full-time equivalent metric for inpatient time, and under half (13/30, 43%) reported the use of critical-care billing. Most respondents (26/30, 87%) endorsed that inpatient attendings frequently complete documentation/sign notes outside of normal daytime hours. During night call, attendings commonly spend 30 minutes-2 hours on patient care-related phone calls between 5 and 10 pm (24/30, 80%) and receive 1-3 patient care-related phone calls after 10 pm (21/30, 70%). Faculty burnout was the biggest inpatient-specific challenge before the coronavirus disease 2019 (COVID-19) pandemic (25/30, 83%), and concern about faculty well-being during the COVID-19 pandemic was reported in nearly all respondents (28/30, 93%). DISCUSSION: Academic child neurology programs in North America implement varied models for inpatient care delivery and face common challenges. The information presented in this study serves to stimulate discussion, help optimize operations, and encourage novel approaches to accomplish work and advance careers in academic inpatient child neurology.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".