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Record W2805605421 · doi:10.1503/cjs.014417

The current state of resident trauma training: Are we losing a generation?

2018· article· en· W2805605421 on OpenAlexaffvenueabout
Paul T. Engels, Nori Bradley, Chad G. Ball

Bibliographic record

VenueCanadian Journal of Surgery · 2018
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsMcMaster UniversityUniversity of CalgaryUniversity of British Columbia
Fundersnot available
KeywordsMedicineTrauma careCall to actionCompetence (human resources)Trauma surgeryMedical emergencyGerontologyFamily medicineSurgery

Abstract

fetched live from OpenAlex

SUMMARY: General surgeons provide life-saving trauma care to a large portion of Canadians. Although trauma care has evolved significantly over the last few decades and now requires fewer operations, when a life-saving operation is required the expectation of competence to perform this operation has not been reduced. A recent study from the United States found decreased resident case-log volumes of trauma operations. Such findings raise the alarm of declining exposure of residents to trauma operations and beg the question of whether graduating residents are competent to care for trauma patients. Examination of the Canadian setting reveals a dearth of published information about the actual exposure of Canadian general surgery residents to trauma care. With the forthcoming evolution of general surgery education into competency-based medical education, we sound a call to action to ensure that all graduating general surgeons are able to provide the care that both the Royal College of Physicians and Surgeons of Canada and the Canadian public demand.

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 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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.972
Threshold uncertainty score0.287

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 head0.214
GPT teacher head0.338
Teacher spread0.124 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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

Citations13
Published2018
Admission routes3
Has abstractyes

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