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Record W4223533533 · doi:10.1017/cjn.2022.11

The Inception of the Canadian Medical Student Interest Group in Neurosurgery (CaMSIGN): A Student-Led Platform Dedicated to Neurosurgical Education, Research, Mentorship, and Advocacy

2022· article· en· W4223533533 on OpenAlexaffvenueabout
Saman Arfaie, Pedram Laghaei Farimani, Mohammad Sadegh Mashayekhi, Benyamin Hakak‐Zargar, Mostafa Fatehi, Jenna Smith‐Forrester, Nicholas Sader, Mojgan Hodaie, Rolando F. Del Maestro, Sunit Das

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2022
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsUniversity of TorontoDalhousie UniversityUniversity of CalgaryUniversity of British ColumbiaMcGill University
Fundersnot available
KeywordsMentorshipNeurosurgeryMedicineMedical educationInterest groupPolitical scienceSurgery

Abstract

fetched live from OpenAlex

The Inception of the Canadian Medical Student Interest Group in Neurosurgery (CaMSIGN): A Student-Led Platform Dedicated to Neurosurgical Education, Research, Mentorship, and Advocacy - Volume 50 Issue 2

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.355
Threshold uncertainty score0.706

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0100.006
Scholarly communication0.0090.002
Open science0.0030.011
Research integrity0.0100.013
Insufficient payload (model declined to judge)0.0910.023

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.

Opus teacher head0.080
GPT teacher head0.378
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations4
Published2022
Admission routes3
Has abstractyes

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