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Record W4213104153 · doi:10.1093/jcag/gwab049.092

A93 USEFULNESS OF A NOVEL SMARTPHONE APP IN GASTROINTESTINAL ENDOSCOPY TO TRACK PROCEDURE NUMBERS AND THERAPEUTIC INTERVENTIONS

2022· article· en· W4213104153 on OpenAlexaffabout
David Yi Yang, Thomas Krahn, C Wang, J Decanini-Trevino, Shawn Wasilenko, Karen I. Kroeker, Andrea Dávila-Cervantes, D C Baugmart, Aldo J. Montaño‐Loza, Brendan P. Halloran, Sergio Zepeda-Gómez

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2022
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTherapeutic endoscopyEndoscopyMedicinePsychological interventionTracking (education)Medical physicsGeneral surgerySurgeryPsychologyNursing

Abstract

fetched live from OpenAlex

Abstract Background Endoscopy teaching is an integral part of gastroenterology (GI) training. Though the number of completed endoscopic procedures does not equate competency, procedure tracking is useful for monitoring an individual’s learning progress. Currently, procedure tracking is typically done on an informal basis using paper or electronic spreadsheets. These methods are non-standardized and may not be shareable between trainees and their programs. Endostation is a smartphone app created by the University of Alberta Therapeutic Endoscopy Program to facilitate the tracking of endoscopic procedures. The app allows trainees to record the number of endoscopies and details such as cecal intubation (CI), ERCP cannulation, and therapeutic interventions. Data can be accessed by users via the app and website (www.endostation.ca), allowing for close monitoring of trainees’ learning progress. Aims Our primary objective was to evaluate the usefulness of the app for tracking the number of endoscopic procedures and therapeutic interventions. Our secondary objective was to evaluate the acquisition of endoscopy skills based on quality endoscopic parameters such as CI rate and ERCP cannulation rate. Methods One therapeutic endoscopy fellow and two GI residents were recruited for the study. Participants were asked to document their procedures over the study period (9-month for therapeutic endoscopy fellow, 12-month for GI residents). Total number of procedures was summed for each trainee. Acquisition of endoscopy skills was tracked by comparing success rates of CI and ERCP cannulation at different points within the study period. Results The therapeutic endoscopy fellow recorded 415 cannulation attempts, 209 sphincterotomies, 282 stone extractions, 71 plastic stent placements, and 37 metal stent placements. There was a significant difference in the cannulation success rate when comparing the 1st trimester and the 3rd trimester of the study period (68% vs 85%; p= 0.0012) (Fig 1). The two GI residents respectively recorded 335 and 170 colonoscopies plus 454 and 305 gastroscopies. Resident 1 recorded 58 polypectomies, 9 esophageal variceal banding, and 16 non-variceal hemostasis. Resident 2 recorded 17 polypectomies, 12 esophageal variceal banding, and 9 non-variceal hemostasis. The CI success rate was significantly higher for both residents when comparing the first 4 months of training vs the last 4 months [24% vs 88% for resident 1 (p=0.00001); 15% vs 42% for resident 2 (p= 0.001)] (Fig 1). Conclusions The smartphone app (Endostation) was a useful tool for endoscopic procedure tracking. Data from the app was useful in demonstrating improvement in CI rate and ERCP cannulation rate over the study period. 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)0.002
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.002

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 head0.022
GPT teacher head0.265
Teacher spread0.242 · 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".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

Explore more

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