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Record W2885320569 · doi:10.1503/cjs.007417

Beyond just the operating room: characterizing the complete caseload of a tertiary acute care surgery service

2018· article· en· W2885320569 on OpenAlexaffvenueabout
Theunis Jean Van Zyl, Patrick Murphy, Laura Allen, Neil Parry, Ken Leslie, Kelly Vogt

Bibliographic record

VenueCanadian Journal of Surgery · 2018
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineProspective cohort studyEmergency medicineEmergency departmentMedical diagnosisTertiary careCohortGeneral surgerySurgeryInternal medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Most studies evaluating acute care surgery (ACS) models of care for patients with emergency general surgery (EGS) conditions have focused on patients who undergo surgery while admitted under the care of the ACS service. The purpose of this study was to prospectively examine the case mix of admissions and consultations to an ACS service at a tertiary centre to identify the frequency and distribution of both operatively and nonoperatively managed EGS conditions. METHODS: In this prospective cohort study, we evaluated consecutive patients assessed by the ACS team between July 1 and Aug. 31, 2015, at a large Canadian tertiary care centre. This included all consultations and outside hospital transfers. Diagnoses, demographic characteristics, comorbidities, intervention(s), complications, readmission and in-hospital death were captured. RESULTS: < 0.001). Bowel obstruction (37 patients [21.0%]) was the most common reason for admission, followed by wound/abscess (24 [13.6%), biliary disease (24 [13.6%]) and appendiceal disease (23 [13.1%]). Rates of 30-day return to the emergency department and readmission were 17.0% and 9.1%, respectively, and the in-hospital mortality rate was 1.7%. CONCLUSION: Acute care surgery teams care for a wide breadth of disease, a considerable amount of which is managed nonoperatively.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.065
GPT teacher head0.276
Teacher spread0.211 · 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

Citations11
Published2018
Admission routes3
Has abstractyes

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