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Record W4318995017 · doi:10.1503/cjs.020821

The next frontier of acute care general surgery: fellowship training

2023· article· en· W4318995017 on OpenAlexaffvenueabout
Paul T. Engels, Jennie Lee, Timothy Rice, Rahima Nenshi, Chad G. Ball, Morad Hameed, Sandy Widder, Samuel Minor, Najma Ahmed, Neil Parry, Kelly Vogt

Bibliographic record

VenueCanadian Journal of Surgery · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsMcMaster UniversityOakville-Trafalgar Memorial HospitalUniversity of CalgaryUniversity of AlbertaUniversity of British ColumbiaUniversity of TorontoDalhousie UniversityWestern University
Fundersnot available
KeywordsMedicineFrontierMEDLINEGeneral surgeryFamily medicineMedical education

Abstract

fetched live from OpenAlex

Acute care surgery (ACS) is an area of surgical specialization within general surgery and a model for clinical care delivery that has proliferated over the last 2 decades. Models of ACS in Canada exist in both academic and community settings and are used to manage patients in need of emergency general surgery (EGS) care, with or without the provision of trauma care. The implementation of the ACS model has changed the landscape of patient care, surgical education and the workforce, providing an option for some general surgeons to exclude EGS care from their regular practice. The rise of ACS as a concentration of surgical skill and content expertise has resulted in the establishment of dedicated ACS fellowship training programs. This is a landmark in the evolution of general surgery, as well as a stepping stone on the path to improving patient care, surgical education and scholarly endeavour in this field.

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)0.006
metaresearch head score (Gemma)0.014
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.005
Scholarly communication0.0060.005
Open science0.0010.008
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0280.005

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 head0.084
GPT teacher head0.271
Teacher spread0.187 · 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
GenreOther

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

Citations2
Published2023
Admission routes3
Has abstractyes

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Same venueCanadian Journal of SurgerySame topicCardiac, Anesthesia and Surgical OutcomesFrench-language works237,207