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Record W2033737638 · doi:10.1177/155335060501200110

Effective Training and Assessment of Surgical Skills, and the Correlates of Performance

2005· article· en· W2033737638 on OpenAlex
Stanley J. Hamstra, Adam Dubrowski

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

VenueSurgical Innovation · 2005
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsThe Wilson CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineCurriculumMedical educationMedical physicsPhysical therapyPsychology

Abstract

fetched live from OpenAlex

This report briefly describes the University of Toronto Surgical Skills Centre and summarizes research in technical skills conducted at that site. This includes work on curriculum evaluation, the development and validation of assessment instruments, the retention of technical skills after training, and the prediction of success in surgery. These studies benefited from the large number of participants made available by the Surgical Skills Centre, allowing for randomized controlled studies or correlation studies where larger numbers are necessary for adequate statistical power. Recent emphasis has been on the further development of objective means of assessment and the exploration of correlates of surgical performance. Ongoing research is also aimed at simulator validation.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.664
Threshold uncertainty score0.199

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.022
GPT teacher head0.329
Teacher spread0.307 · 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