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Record W2620582323 · doi:10.1017/cjn.2017.191

P.107 Standardizing resident operative-case logging: the first step of a prospective national study of resident operative volume

2017· article· en· W2620582323 on OpenAlexaffvenueabout
Alexander Winkler-Schwartz, Mark Bigder, Ayoub Dakson, Christopher Elliot, Daipayan Guha, Christian Iorio‐Morin, M Kameda-Smith, Pascal Lavergne, Serge Makarenko, M Taccone, MK Tso, Bingqi Wang, Joyce L Atkinson

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2017
Typearticle
Languageen
FieldMedicine
TopicDigital Imaging in Medicine
Canadian institutionsCalgary Laboratory ServicesSherbrooke O.E.M (Canada)Toronto Public HealthAlberta Hospital EdmontonSystems, Applications & Products in Data Processing (Canada)Vancouver Biotech (Canada)University of Winnipeg
Fundersnot available
KeywordsNeurosurgeryMedicineMedical physicsMedical educationSurgery

Abstract

fetched live from OpenAlex

Background: No standardized method of resident operative-case logging exists. Our study sought to develop a standardized form used by residents to log operative-cases. Methods: Members of the Canadian Neurosurgery Research Collaborative (CNRC), a national resident-led research organization have created a standardized document based on the current Royal College objectives for operative procedures (section 5). Modifications to structure and content will be guided via consensus from Canadian neurosurgery program-directors. Results: Program directors in each CNRC collaborative institution will be asked to modify the standardized form. The CNRC currently involves thirteen of the fourteen Canadian neurosurgery residency programs. Additional consensus, if necessary, can be reached at the Royal College meeting for program directors of neurosurgery March 20 th 2017. Conclusions: A standardized operative-case log represents the first step in a prospective study towards compiling operative volume of all Canadian neurosurgical residents over one academic year. Such data will be essential to guide informed decisions with regard to Royal College requirements as Canadian neurosurgical programs transition to a competency based framework.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.012
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.057
GPT teacher head0.350
Teacher spread0.293 · 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; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2017
Admission routes3
Has abstractyes

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