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Record W1986423456 · doi:10.1007/s00268-007-9143-y

Objective Assessment of Technical Performance

2007· review· en· W1986423456 on OpenAlexaff
Gerald M. Fried, Liane S. Feldman

Bibliographic record

VenueWorld Journal of Surgery · 2007
Typereview
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsAbdominal surgeryVascular surgeryCardiac surgeryCardiothoracic surgeryMedicineSurgery

Abstract

fetched live from OpenAlex

Technical skills are essential to the practice of surgery. They can be taught in the operating room and in the surgical skills laboratory. The use of simulators allows the development of reproducible curricula with measurement of performance using objective metrics. The goal of those designing metrics for the simulation laboratory should be to establish measures that are consistent with those of high quality surgery in the operating room. Once these metrics have been shown to be reliable, valid, practical to use, and meaningful to the learner, they can form the basis of a learning program based on the acquisition of proficiency. Performance in the skills laboratory should ultimately be predictive of performance in the clinical setting.

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)0.003
metaresearch head score (Gemma)0.008
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: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0050.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.171
GPT teacher head0.439
Teacher spread0.267 · 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
GenreReview

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

Citations101
Published2007
Admission routes1
Has abstractyes

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