A novel smartphone application for the tracking of procedural numbers and trainee experience in gastrointestinal endoscopy
Bibliographic record
Abstract
OBJECTIVES: The tracking and documentation of procedures in gastrointestinal endoscopy including therapeutic interventions is an essential but challenging process. The University of Alberta has developed a smartphone app to help facilitate this task. This study evaluated the functionality, usefulness, and user satisfaction of this app. METHODS: Four Gastroenterology (GI) residents and two therapeutic endoscopy fellows participated in the study. The trainees submitted all their data into the app from the procedures in which they participated hands-on for one year, data was collected and analyzed on the app and the website associated with it. RESULTS: Trainees were able to register the procedures immediately after each procedure without difficulty, this data was available to be reviewed at anytime in the app and associated website. Furthermore, the data collected was able to be transformed into tables and graphs on the app website. The total number of procedures and therapeutic interventions performed were easily accessed in the app and website at anytime. The app facilitated the calculation of the cecal intubation rate in colonoscopy and the cannulation rate in ERCP for the therapeutic endoscopy trainee. Trainees reported excellent experience with the app capabilities. CONCLUSIONS: A novel smartphone app was useful in collecting meaningful data submitted by gastrointestinal endoscopy trainees, furthermore, through an associated website, it was capable to create graphs and tables to show and facilitate the calculation of meaningful data such as key performance indicators.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".