Objective Evaluation of Competence in Flexible Sigmoidoscopy
Bibliographic record
Abstract
Purpose: Determination of competence in endoscopy is important in any endoscopy training program. While objective assessments have been attempted they have not been standardized. A program to train registered nurses (RNs) to perform flexible sigmoidoscopy (FS) has been developed in Ontario, Canada in order to create increased endoscopy capacity to screen for colorectal cancer. Objectives: To develop a standardized method to determine technical competence based on a simulator experience. Methods: Simulator training was undertaken before the RNs performed procedures on patients. Checklists and global assessments to assess performance on the simulator were developed incorporating simulator-generated parameters and technical characteristics used in the surgical literature. Checklists included measurement of procedure duration, insertion depth, percent mucosa visualized, and percent of time in red-out. Results: Six RN trainees each performed 10 FSs using the simulator over two days. The simulator provided 18 different endoscopic scenarios divided by an expert endoscopist into categories for degree of technical difficulty (easy, average, and difficult). Figure 1 shows that the time to perform procedures decreased with trainee experience for the average and technically difficult scenarios. There was no distinction seen for the technically easy procedures. Other trends for improved mucosal visualization, depth of endoscope insertion, and time spent in red-out have also been detected. (Data not shown.)FigureConclusions: These observations indicate that objective evaluations of endoscopic competence are possible on simulator experience, although they need to be validated. These tools may predict skill transfer to patients and this will be assessed as the students gain experience performing FS on patients. [figure1]
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 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".