Education Research: An experiential outpatient teleneurology curriculum for residents
Bibliographic record
Abstract
OBJECTIVE: Telemedicine is rapidly becoming a major vehicle of delivering neurologic care to patients who have limited access to subspecialists and exaggerated travel hardship. However, neurology residents receive little to no training in telemedicine in outpatient clinics. METHODS: We piloted, to our knowledge, the first formalized, experiential outpatient teleneurology curriculum. Neurology residents in their third and fourth postgraduate years (PGY3 and PGY4) at the University of California San Francisco completed an interactive lecture and 4 weeks of teleneurology clinics at the San Francisco Veterans Affairs Medical Center. Change in residents' telemedicine knowledge and perspectives on the utility, challenges, benefits, and future practice implementation of teleneurology were evaluated in 11 residents using precurriculum and postcurriculum quizzes and surveys after 2 of 4 weeks on the rotation. RESULTS: 0.04). All residents felt more competent using telemedicine for patient care in their eventual career. CONCLUSION: Our formal didactic and clinic-based teleneurology curriculum for neurology residents, which shared core themes suggested by the 2017 American Academy of Neurology Telemedicine Work Group's published recommendations, showed a statistically significant improvement in knowledge and perspectives about the promise and limitations of teleneurology practice, as well as increased comfort levels in future implementation.
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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 | 0.004 | 0.006 |
| 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".