Evidence map›Paper›PMID 42381653›Full record

ArticleNeurology. Education2026

Education Research: Educational Informatics: Characterizing Child Neurology Resident Clinical Experiences Using Electronic Health Record Data.

Rachel Gottlieb-Smith, Joshua Adams, Miya E Bernson-Leung, Brian Stamm, Mark V Mai, Robert Thompson Stone, Elizabeth T Troy, Kathryn Xixis, Bittu Majmudar-Sheth, Louis T Dang and 1 more

Abstract read
In one paragraph

Article in Neurology. Education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Rachel Gottlieb-SmithDepartment of Pediatrics, University of Michigan, Ann Arbor.ORCID https://orcid.org/0000-0002-6066-5966
Joshua AdamsDepartment of Pediatrics, University of Michigan, Ann Arbor.ORCID https://orcid.org/0000-0002-9556-9499
Miya E Bernson-LeungDepartment of Neurology, Boston Children's Hospital, Harvard Medical School, MA.ORCID https://orcid.org/0000-0002-7766-2323
Brian StammDepartment of Neurology, University of Michigan, Ann Arbor.ORCID https://orcid.org/0000-0002-0862-9650
Mark V MaiDepartment of Pediatrics, Emory University School of Medicine, Atlanta, GA.ORCID https://orcid.org/0000-0003-0346-5818
Robert Thompson StoneDepartment of Neurology, Golisano Children's Hospital, University of Rochester, NY.ORCID https://orcid.org/0000-0003-1062-854X
Elizabeth T TroySection of Child Neurology, Department of Pediatrics, Children's Hospital of Colorado, University of Colorado, Denver.ORCID https://orcid.org/0000-0001-5323-428X
Kathryn XixisDepartment of Neurology, University of Virginia School of Medicine, Charlottesville.ORCID https://orcid.org/0000-0002-6423-1065
Bittu Majmudar-ShethDepartment of Pediatrics, Indiana University School of Medicine, Indianapolis; and.ORCID https://orcid.org/0000-0003-3206-0414
Louis T DangDepartment of Pediatrics, University of Michigan, Ann Arbor.ORCID https://orcid.org/0000-0003-4853-4952
Adam C DziornyDepartment of Pediatrics, Golisano Children's Hospital, University of Rochester, NY.ORCID https://orcid.org/0000-0002-3392-2795

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Objectives: Gaps in preparedness for independent practice are common among recent graduates. Clinical experiences serve as the foundation for learning during residency, yet the breadth and variability of clinical exposure of child neurology residents remain poorly characterized. To inform programmatic curricular development and precision medical education (PME), we must better characterize learners' clinical exposure during training. We aimed to use electronic health record (EHR) data to catalog resident clinical experiences, linking diagnostic codes to educational benchmarks. Methods: We performed a retrospective cohort study of 11 child neurology residents at a single institution in their neurology years of training between July 2024 and May 2025. We used an established algorithm to determine which patient encounters were most meaningful for resident education, based on EHR interaction, and extracted International Classification of Disease (ICD)-10 codes from these encounters. We mapped ICD-10 categories to American Board of Psychiatry and Neurology (ABPN) child neurology certification examination content areas. We identified the most frequent ICD-10 categories seen in inpatient and outpatient settings. We also identified the frequency of ABPN content areas seen across encounters and compared with their relative frequency on the certification examination. Results: We identified 3,109 meaningful outpatient and 4,469 meaningful inpatient encounters over an 11-month period. The crosswalk between ICD-10 categories and ABPN content areas contains 385 ICD-10 categories mapped to ABPN content domains. There was 90.0% ICD-10 category capture for outpatient encounters and 73.8% for inpatient encounters. The most frequent ICD-10 categories were "epilepsy and recurrent seizures" and "migraine" (outpatient) and "cerebral infarction" and "convulsions" (inpatient). More than half of ABPN content areas were underrepresented in clinical exposure compared with the certification examination. There was overrepresentation of 3 ABPN content areas: "epilepsy and episodic disorders," "vascular neurology," and "headache and pain disorders." Discussion: A novel crosswalk between ICD-10 categories and ABPN content areas for child neurology enables transformation of EHR-coded data to educationally meaningful information. Using this crosswalk to characterize resident experiences from EHR data may inform rational curricular design and PME. Programs may use these data to optimize rotations and target experiential learning gaps at individual and program levels, resulting in better preparation for autonomous practice.

Identifiers

PMID42381653
PMCPMC13316738

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.