Neurology International Residents Videoconference and Exchange (NIRVE) (P4.299)
Bibliographic record
Abstract
OBJECTIVE: To provide peer-led knowledge transfer, encourage exposure to Global Health in neurology and promote international collaboration among neurology trainees. BACKGROUND: In 2006, the World Health Organization recognized neurological disorders as a major contributor to the global burden of diseases and a leading cause of loss in disability-adjusted life-years (DALYs). However, many neurology training programs in North America lack a Global Health component to their curriculum. On-the-ground international clinical and research exchanges abroad are accessible and undertaken by few residents. The Neurology International Residents Videoconference and Exchange (NIRVE) is a resident initiative from the University of Toronto that promotes neurology and global health education using the advantages of a videoconferencing system. DESIGN: NIRVE currently unites 5 international sites (Toronto, Canada; Grenoble, France; St Petersburg, Russia; Addis Ababa, Ethiopia; and San André, Brazil). Each session is attended by an average of 30 residents and consists of a main presentation on rotating topics on the practice of neurology or issues in global health, followed by a neuroradiology case. Interactive discussion periods are incorporated and used to promote dialogue on the current state and various advances and challenges in neurology, both locally and internationally. RESULTS: A recent survey of resident participants demonstrated that over 80% of participants found the rounds relevant and useful for their training, despite regional differences. Through bonds forged via the NIRVE program, residents have also been able to arrange international exchanges at other participating sites to further experience and learn from differences in the practice of neurology abroad. CONCLUSIONS: NIRVE has allowed neurology residents to connect with peers internationally. Its impact has been taken from the virtual world to on-the-ground clinical practice. It is a powerful tool for knowledge transfer that can influence future neurologists’ practice by bridging distances across physical, political and socioeconomic borders.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.297 | 0.055 |
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".