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Record W3217060952 · doi:10.9778/cmajo.20200306

Structures, processes and models of care for emergency general surgery in Ontario: a cross-sectional survey

2021· article· en· W3217060952 on OpenAlexaffvenueabout
Graham Skelhorne‐Gross, Rahima Nenshi, Angela Jerath, David Gómez

Bibliographic record

VenueCMAJ Open · 2021
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsHamilton General HospitalMcMaster UniversityUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsCross-sectional studyMedicineMedical emergencyPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Emergency general surgery (EGS) patients require urgent surgical evaluation and intervention for various conditions, such as infectious or obstructive diseases of the gastrointestinal tract. We aimed to characterize the structures and processes that are relevant to the delivery of EGS care across Ontario hospitals and to evaluate the availability of critical resources at hospitals with formal EGS models. METHODS: Between August 2019 and July 2020, we conducted a cross-sectional survey of Ontario hospitals that offered urgent general surgery (defined as the ability to provide nonelective surgical intervention within 24 to 48 hours of presentation) to adults. People with intimate knowledge of their hospital's EGS program completed a Web-based or telephone survey characterizing the program's organizational structure and staffing, operating room availability, interventional radiology and interventional endoscopy availability, intensive care unit availability and staffing, and regional participation. Their responses were compiled and comparisons were made between hospitals with and without formal EGS models of care, as well as between hospitals based on size and academic status. RESULTS: = 0/25]). INTERPRETATION: The structures and processes available to care for patients requiring EGS in Ontario were highly variable between hospitals. Hospitals with formal EGS models were more likely to have access to key resources.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.693
Threshold uncertainty score0.878

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.118
GPT teacher head0.367
Teacher spread0.249 · 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 designObservational
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

Citations8
Published2021
Admission routes3
Has abstractyes

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