MétaCan
Menu
Back to cohort
Record W4283010016 · doi:10.26685/urncst.302

Investigating the Use of 3D-Printing in the Medical Education Curriculum in Otolaryngology: A Protocol Paper

2022· article· en· W4283010016 on OpenAlexaff
Gareth Leung, Arthur Travis Pickett, Michael Bartellas, Ariana Milin, Matthew Bromwich, Risa Shorr, Lisa Caulley

Bibliographic record

VenueUndergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsOttawa HospitalMcGill UniversityUniversity of Ottawa
Fundersnot available
KeywordsOtorhinolaryngologyProtocol (science)CurriculumMedical education3D printingMedicinePsychological intervention3d printedMedical physicsAlternative medicineEngineeringPsychologyPathologySurgeryBiomedical engineeringMechanical engineeringNursingPedagogy

Abstract

fetched live from OpenAlex

Three-dimensional (3D) printing has been used in recent years to produce educational materials in medicine. Recent studies have found that fields such as otolaryngology may benefit from the use of 3D printing in teaching medical students and residents. Our team will conduct a systematic review to survey the current uses of 3D printing interventions in otolaryngology. We aim to assess how similar the 3D printed models are to human anatomy, their surgical utility, and educational uses.

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)0.058
metaresearch head score (Gemma)0.076
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: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.058
Threshold uncertainty score0.309

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0580.076
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0050.008
Bibliometrics0.0050.006
Science and technology studies0.0030.003
Scholarly communication0.0040.004
Open science0.0030.004
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0380.007

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 head0.056
GPT teacher head0.403
Teacher spread0.347 · 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
GenreProtocol

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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueUndergraduate Research in Natural and Clinical Science and Technology (URNCST) JournalSame topicAnatomy and Medical TechnologyFrench-language works237,207