P.150 The Canadian Medical Student Interest Group in Neurosurgery (CaMSIGN) platform: a retrospective study on Canadian medical students
Bibliographic record
Abstract
Background: The Canadian Medical Student Interest Group in Neurosurgery (CaMSIGN) is the first neurosurgery platform of its kind in Canada. Methods: In this retrospective study, data from CaMSIGN’s online platforms have been collated from February 2021 to the present and analyzed to show trends in user engagement. Results: CaMSIGN events generated 1,575 views on YouTube (384 from Canada). The total watch time was 170.3 hours, of which 43.9 hours were Canadian (28.5%). The total views normalized by the total number of students interested in neurosurgery was 17.12 hours. The normalized Canadian view was 4.17. 717 people follow the CaMSIGN Facebook account (normalized= 7.79). 152 people follow our Instagram (normalized= 1.65). 338 people follow our Twitter (normalized= 3.67). This number is comparable to that of estimated practicing neurosurgeons in Canada (333). A total of 32,974 people visited the Twitter page, with a monthly average of 2747.8. Lastly, the campaign website has had 5,811 visitors since its launch in June 2021 with a monthly average of 695.57 visitors. The number of website visitors has increased at a rate of 3.1327 visitors/month. Conclusions: Through this initiative, our aim has been to model a pan-Canadian approach to neurosurgery.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".