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Record W4401760065 · doi:10.2196/50128

Live Streaming of the Professor’s Ward Rounds in Undergraduate Neurology Education: Usability Study

2024· article· en· W4401760065 on OpenAlexvenueno aff
Kenji Sekiguchi, Seiji Kawano, Norio Chihara, Seimi Satomi‐Kobayashi, Eiichi Maeda, Riki Matsumoto

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
FundersMinistry of Education, Culture, Sports, Science and Technology
KeywordsVideoconferencingUsabilityMedical educationThe InternetMedicinePsychologyMultimediaComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: Although neurology department ward rounds are among the most important medical education exercises in Japan, they have several issues. Patients may find it unpleasant to undergo repeated neurological tests, especially when in the presence of several students. Only the front row of students can closely observe the examination findings; moreover, students were prohibited from contacting patients altogether during the COVID-19 pandemic. One possible solution is to use commercial videoconferencing systems. However, Japanese patients are reluctant to have their medical information or video footage of them sent outside of the hospital via the internet. OBJECTIVE: The study aimed to confirm the feasibility of conducting remote teaching rounds using an in-house web conferencing system in which the patients' personal data are securely protected. This study also explored whether using remote rounds alongside face-to-face participation would enhance learning. METHODS: We created an on-premises videoconferencing system using an open-source app. To perform video ward rounds, the professor wore a wireless microphone while leading routine in-person rounds and the attending physician carried a tablet device linked to a web conference, allowing students in another room to watch the rounds on a live stream. In total, 112 of 5th-year students who entered their 1-week neurology rotation between 2021 and 2022 were instructed to participate in 1-hour in-person and remote rounds. Students were given questionnaires to evaluate their satisfaction and the educational effects of the remote rounds. RESULTS: The remote ward rounds were conducted easily with no interference with the in-person rounds, nor any complaints from the patients. Each examination technique was explained by another teacher to the students who participated in remote rounds in the conference room. Characteristic neurological findings, such as plantar reflexes (Babinski sign), which are usually seen only by close observers during in-person rounds, could be visualized under magnification by all students. The postexperience survey (82/112, 73% response rate) showed that the mean score of participants' satisfaction was 3.94 (SD 0.83; excellent 5 and poor 1). No participant scoring 1 was noted. The proportion of students who observed 6 representative abnormal neurological findings (Babinski sign, hyperreflexia, cerebellar ataxia, involuntary movement, muscular weakness, and abnormality in sensory examination) increased by 22% (18/82, range 13-24) compared to in-person rounds alone. When self-rating the learning value, 43% (35/82) of the students answered that remote rounds are equally as valuable as in-person rounds, while 32% (26/82) preferred remote rounds. CONCLUSIONS: Live-streaming of neurology ward rounds using a secure in-house web conferencing system provides additional learning experience without concerns regarding leakage of patient information. This initiative could enhance neurology learning before entering a clinical clerkship.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.092
GPT teacher head0.503
Teacher spread0.411 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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