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Record W3004445643 · doi:10.1503/cjs.010218

Surgeon attitudes toward point of care ultrasound for biliary disease: a nationwide Canadian survey

2020· article· en· W3004445643 on OpenAlexaffvenueabout
Jennifer Koichopolos, Richard Hilsden, Frank Myslik, Drew Thompson, Jeremy Vandelinde, Rob Leeper

Bibliographic record

VenueCanadian Journal of Surgery · 2020
Typearticle
Languageen
FieldMedicine
TopicGallbladder and Bile Duct Disorders
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineCholecystitisBiliary diseaseBiliary colicAcute cholecystitisDiseaseUltrasoundGallbladder diseaseGeneral surgeryCholecystectomyGallbladderFamily medicineInternal medicineRadiology

Abstract

fetched live from OpenAlex

Summary: Multiple studies confirm that point of care ultrasound (PoCUS) has a high sensitivity and specificity for cholelithiasis and cholecystitis. However, there is poor perceived reliability of biliary PoCUS by surgeons. This survey was performed to assess surgeons’ opinions on using PoCUS in gallstone disease and barriers that exist for its institution. The majority (60.3%) of respondents reported a total lack of confidence in PoCUS for the diagnosis of biliary disease. Most felt the sensitivity of PoCUS was poor and had concerns about the user-dependent nature of the test and the lack of imaging details provided. If offered ideal clinical/laboratory findings with PoCUS results, only 4.7% of surgeons would definitely operate for unremitting biliary colic and 5.4% for cholecystitis. The ability to replicate findings independently increased confidence in clinical decision-making. Our findings suggest there is substantial distrust in biliary PoCUS but that specific ultrasound training for the surgical workforce may prove tremendously beneficial for its utilization.

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)0.004
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 score0.520
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.057
GPT teacher head0.269
Teacher spread0.212 · 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

Citations7
Published2020
Admission routes3
Has abstractyes

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