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Framework for Systematic Training and Assessment of Technical Skills

2007· article· en· W2079767916 on OpenAlexaff
Rajesh Aggarwal, Teodor Grantcharov, Ara Darzi

Bibliographic record

VenueJournal of the American College of Surgeons · 2007
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMedicineMedical educationTraining (meteorology)Medical physics

Abstract

fetched live from OpenAlex

Aggarwal, Rajesh MA, MRCS; Grantcharov, Teodor P. MD, PhD; Darzi, Ara KBE, MD, FACS, HonFREng, FMedSci Author Information

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)0.295
metaresearch head score (Gemma)0.378
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.295
Threshold uncertainty score0.870

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2950.378
Meta-epidemiology (narrow)0.0050.004
Meta-epidemiology (broad)0.0090.016
Bibliometrics0.0260.017
Science and technology studies0.0070.007
Scholarly communication0.0100.008
Open science0.0090.018
Research integrity0.0080.011
Insufficient payload (model declined to judge)0.0100.004

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 head0.035
GPT teacher head0.368
Teacher spread0.334 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations175
Published2007
Admission routes1
Has abstractyes

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