A63 COLONOSCOPY QUALITY IN COLORECTAL CANCER SCREENING: HOW BEST TO CAPTURE THE DATA?
Bibliographic record
Abstract
Colonoscopy quality indicators have been developed to ensure performance of accurate and safe colonoscopies as part of screening for colorectal cancer. Measurement of these indicators requires timely recording of data during the colonoscopy procedure to allow for subsequent calculation of indicator rates. While various data capture methods currently exist, ranging from paper-based to electronic record systems, there is very little guidance available regarding the necessary requirements for accurate data entry into these systems. In this outcomes project, our objectives were to assess both the uptake and barriers to implementation of quality data capture using a standardized bedside data collection form. A pilot project was conducted in the Coaldale Screening Centre in southern Alberta over a 6-month period in 2016. This centre is a stand-alone endoscopy unit solely dedicated to performing colonoscopies for colorectal cancer screening. A standardized data collection form was developed to capture key quality indicators of interest as well as information regarding colonic polyps removed. The endoscopy theatre nurse completed the form during each procedure. Data from the completed forms were then entered into a centralized electronic reporting system (Synoptec). Site visits, surveys and interviews were carried out to determine satisfaction with the method and to identify barriers to broader implementation of this quality initiative in other units. A manual audit was performed to determine the accuracy of data collection and entry. During the study timeframe, 660 cases were entered into Synoptec and available for analysis. Feedback from the site visits, interviews and user surveys demonstrated the following concerns a) disparity between the endoscopists and nursing record regarding confirmation of landmarks b) concern over duplicate data entry and, c) extra time required to collect quality data resulting in delayed theatre turnover. An audit was completed on 10% (n=67) of the total cases to determine the level of agreement between the data collected on the standardized form (nurse) to the colonoscopy report (endoscopist). Findings indicated data were comparable for all quality indicators with the exception of withdrawal time; 75% case disparity. 54% of cases had a variance in withdrawal time within 1 minute and 30% were more than 2 minutes. Institution of a colonoscopy quality program requires a culture of quality in the endoscopy unit that facilitates clear and purposeful communication between colonoscopist and theatre nurse, avoids duplication of data entry and builds quality data collection into the main work flow of the endoscopy unit. None
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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 | 0.233 | 0.445 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.018 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 0.009 |
| 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".