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Record W2331875352 · doi:10.15766/mep_2374-8265.9028

Surgery 101 Podcast: Episodes 41–50

2011· article· en· W2331875352 on OpenAlex
Jonathan White, Daniel W. Birch, Marcia Clark, Chris de Gara, Shahzeer Karmali, Ruth McGaffigan, Dereck Mok, Dan Schiller

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueMedEdPORTAL · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLibrary scienceMedicineComputer science

Abstract

fetched live from OpenAlex

Abstract This resource is a series of podcasts intended to serve as brief introductions to and reviews of surgical topics for medical students. The aim was to cover a single topic in 15–20 minutes so that learners could quickly grasp the basic concepts relating to common surgical problems. Learning objectives are provided for each episode; episodes are divided into chapters and conclude with several key points to summarize the topic. This module contains topics/episodes on enlarged lymph node, adult soft-tissue sarcoma, surgery for morbid obesity, minimally invasive surgery, orthopedic emergencies, hemorrhoids, damage-control surgery, enterocutaneous fistula, trauma in pregnancy, and surgical training. Surgery 101 has been produced since October 2008; it was created by Dr. Parveen Boora and Dr. Jonathan White and is currently produced by the Undergrad Surgery Mobile Podcasting Studio Team with the assistance of the members of the Surgery Department at the University of Alberta.

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.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.277
GPT teacher head0.393
Teacher spread0.116 · 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