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Record W2411365140

Gallbladder disease in northwestern Ontario: the case for Canada's first rural ERCP program.

2011· article· en· W2411365140 on OpenAlexaffabout
Eric Touzin, C. D. Decker, Len Kelly, Bryanne Minty

Bibliographic record

VenuePubMed · 2011
Typearticle
Languageen
FieldMedicine
TopicGallbladder and Bile Duct Disorders
Canadian institutionsNOSM University
Fundersnot available
KeywordsMedicineReferralGeneral surgeryEndoscopic retrograde cholangiopancreatographyCholecystectomyDemographicsPopulationSurgeryDemographyFamily medicinePancreatitisEnvironmental health
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION: The rate of cholecystectomy in northwestern Ontario is double the provincial rate. This paper explores the demographics of cholecystectomy and the role for rural endoscopic retrograde cholangiopancreatography (ERCP) services in the central part of this region. METHODS: We conducted a literature review of ERCP services and cholecystectomy rates, as well as a hospital chart review of patients who underwent laparoscopic cholecystectomies in Sioux Lookout, Ont. We contacted surgeons and gastroenterologists from referral centres in Winnipeg, Man., and Thunder Bay, Ont., for the charts of patients from our catchment area who underwent ERCP. RESULTS: Patients in our region who require urgent and emergent surgery are flown by fixed-wing aircraft to referral centres in Winnipeg and Thunder Bay for assessment and surgery. The rate of ERCP in our population is 150 in 100 000, which is threefold that of other populations, and our cholecystectomy rate is the highest in Ontario. CONCLUSION: Substantial savings in transportation expenses would offset the development costs of an ERCP program and provide more integrated patient care. The volume of patients would support maintenance of competency. This rural area with a high rate of gallbladder disease would benefit from the development of a rural ERCP program.

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 score0.035
Threshold uncertainty score0.466

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 head0.026
GPT teacher head0.223
Teacher spread0.197 · 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

Citations5
Published2011
Admission routes2
Has abstractyes

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