Modern Approach to the Study of Telemedicine Technologies in the Medical Institute
Bibliographic record
Abstract
Telemedicine is being actively introduced into medical practice, however, in order for it to become an effective tool in their hands, a basic knowledge of the possibilities and limitations of modern telemedicine technologies is needed, as well as practical skills in the preparation and conduct of videoconferencing. This led to the need to include a course on the basics of telemedicine technology in the training of medical personnel. The Department of Medical Informatics created the educational module Telemedicine, which is implemented by the Telemedicine Centre of the Medical Institute of the Peoples' Friendship University of Russia. After theoretical lectures, students receive practical skills through business games and conducting videoconferencing. Classes are conducted in accordance with world trends and standards. During the classes we demonstrate to students the technologies of remote interactive learning, in particular television lectures and master classes from the leading clinics of Russia, countries of Europe, India, Brazil and Canada. This practice allows our graduates to maintain contact with their teachers through telemedicine opportunities and participate in videoconferenced postgraduate education with PFUR professors, and international conferences held at PFUR sites. The experience of teaching the senior students of the PFUR Medical Institute is presented.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".