In Reply: A Novel Neurosurgery Virtual Interest Group for Disadvantaged Medical Students: Lessons Learned for the Postpandemic Era
Bibliographic record
Abstract
To the Editor: We appreciate the insight from Barrie and Detchou regarding the early success and future potential of virtual interest groups focused on disadvantaged populations in neurosurgery.1,2 We would like to express our support for furthering the training of future neurosurgeons and agree with the prospect of expanding virtual interest groups such as Neurosurgery Education and Research Group (NERG). With the 2021 to 2022 academic year, we offered our program to a wider array of US-based students through the Medical Student Neurosurgery Training Center, rebranded as Neurosurgery Virtual Education and Research Group (NERVE). This domestic expansion was performed to achieve shared objective mentorship through multi-institutional collaboration on a grander scale and to involve more resident and faculty oversight, who we opine also have the great opportunity to thrive within a virtual interest group system. With the uncertainty of United States Medical Licensing Examination (USMLE) step 1's recent move to a pass/fail system, improving members' research productivity, career development, and networking is as important as ever. Our hope is also to continue growing NERVE by including international medical graduates, who the authors rightly identify as a similarly (and often more-so) disadvantaged population desiring postgraduate neurosurgery education in the United States. This is further amplified by the rise in new US medical schools without home programs and the consistently high competitiveness of the neurosurgery match. As this concept is early in practice, there remains a demand for increased access from virtual interest groups, but variable costs hinder the rapid expansion of NERVE. We hope to inspire others to attempt similar initiatives and encourage correspondence with NERVE to increase involvement in virtual experiences among existing groups as well as conceptualize similar organizations that have the potential to further reduce heterogeneity in neurosurgical mentorship, research, and education among medical students and residents.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.034 | 0.042 |
| Insufficient payload (model declined to judge) | 0.008 | 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".