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Record W3007509215 · doi:10.1093/jcag/gwz047.247

A248 WHAT IS THE MINIMUM TRAINING STANDARD FOR POINT OF CARE INTESTINAL ULTRASOUND? A SINGLE CENTER, PROSPECTIVE, OBSERVATIONAL STUDY TO DEFINE TRAINING STANDARDS

2020· article· en· W3007509215 on OpenAlexaffabout
B Baraty, Cathy Lu, Divine Tanyingoh, Christian Maaser, Kerri L. Novak

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2020
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsMedicineObservational studyGold standard (test)ColonoscopyMagnetic resonance imagingUlcerative colitisUltrasoundProspective cohort studyInflammatory bowel diseaseRadiologyPhysical therapyDiseaseSurgeryInternal medicineColorectal cancer

Abstract

fetched live from OpenAlex

Abstract Background Higher demands for colonoscopy and magnetic resonance imaging (MRI) for long term disease monitoring can be expected as the prevalence of IBD continues to rise in Canada. As resources are perpetually constrained, timely access to effective monitoring strategies important to direct care are increasingly compromised. Intestinal ultrasound provides a cost-effective solution to these challenges. Intestinal ultrasound (IUS) is a patient-centered, accurate modality used during clinic by non-radiologists to enhance clinical decision making. Minimum IUS training standards have yet to be established. Aims The aim of this study is to report a single operator IUS performance characteristics after completion of 4 weeks of training with 100 completed supervised scans. Methods A single center, prospective, observational study over 4 years utilizing a convenience sample of patients presenting to the inflammatory bowel disease (IBD) clinic at the University of Calgary. A single operator compared IUS to gold standard (either colonoscopy, or alternative cross-sectional imaging) with sensitivity, specificity, positive and negative predictive value calculated for each year. Joinpoint regression was performed to analyze the trend for sensitivity and specificity over the study period. Results A total of 235 IUS were performed on 235 individuals diagnosed with IBD between 2013 and 2016. There were individuals with 26 ulcerative colitis and 209 persons diagnosed with Crohn’s disease. There was a non-significant increase in sensitivity and specificity point estimates over the 4 year period (Table 1). The sensitivity, specificity, positive predictive value, and accuracy of IUS in 2016 were 100% (95% CI: 81%-100%), 92% (95% CI: 62%-100%), 94% (95% CI: 72%-99%) and 97% (95% CI: 8%-100%) respectively. Conclusions IUS provision by a gastroenterologist having completed 100 supervised scans during training within an expert center is sufficient, resulting in high accuracy. Although there is some improvement over time, the trend towards improvement over time is not significant. This study provides evidence to inform IUS training programs with a minimum training standard benchmark, imperative with expanding demand and development of new expert centers. Funding Agencies None

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)0.010
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.074
GPT teacher head0.330
Teacher spread0.256 · 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 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".

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Citations0
Published2020
Admission routes2
Has abstractyes

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