SU‐FF‐E‐04: Using the Internet for Real‐Time Education and Knowledge Exchange in Medical Physics
Bibliographic record
Abstract
Purpose: This work provides an update to the ongoing project of Remote Real‐Time Learning. ( http://www.neteinfach.com/rrtl/index.htm ). The goal of the project is to promote the use of Internet to provide classroom style real‐time interactive education in Medical Physics. Method and Materials: This project was started 3 years ago as a collaboration between the Department of Medical Physics at the Toronto‐Sunnybrook Regional Cancer Centre in Canada and the Department of Radiology at the University of Malaya in Malaysia. A class of Medical Physics graduate students at the University of Malaya attended lectures provided by lecturers in Toronto, using the Internet as the main tool of communication. As part of the study, the different methods that can be used to provide real‐time interactive remote education were explored, and various topics including traditional classroom lectures as well as hands‐on workshops were also delivered. Results: Based on our experience, a reasonably stable methodology has been established. This methodology allows a fairly smooth set‐up and conduction of the lectures, at an insignificant cost, while offering flexible convenience to the lecturers as well as the students, despite the widely different time zones. Conclusion: The current plan is to expand the process to allow students at multiple sites of the world to attend the online lectures at the same time. Our project welcomes the participation of both lecturers and students who are interested in taking advantage of the advance of the Internet to promote greater accessibility of quality education in the field.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".