226 Impact of Propofol Sedation on Colonoscopy Quality Metrics: A Population-Based Cohort Study
Bibliographic record
Abstract
INTRODUCTION: The use of propofol during colonoscopy has gained increased popularity due to deeper anesthesia compared to conscious sedation. Whether propofol sedation leads to improved colonoscopy quality metrics is unknown and the aim of our study. METHODS: We examined the association between use of propofol sedation and colonoscopy quality metrics, including adenoma detection rate (ADR), polyp detection rate (PDR), cecal intubation, and perforation, using colonoscopy quality data prospectively collected by the Southwest Regional Cancer Program, a division of Cancer Care Ontario. All colonoscopies performed for any indication across 21 hospitals in Southwest Ontario between April 2017 and December 2018 were identified. Data collected included patient and endoscopist demographics, procedural indication, bowel preparation quality, type of sedation, cecal intubation, polyp detection, and histology. Multi-variable models were built to assess the relationship between propofol sedation and adenoma detection rate (ADR), polyp detection rate (PRD), cecal intubation, and perforation risk. RESULTS: In total, 23,903 colonoscopies were identified, of which 8,533 (35.6%) procedures were performed with propofol sedation. There were no significant differences in ADR (22.6% vs. 21.6%, P = 0.156), PDR (43.9% vs. 42.6%, P = 0.05), or cecal intubation rates (97% vs 96.7%, P 0.192) between the two groups on univariate or multivariate analyses. The event rate for perforation was too low for a meaningful comparison in this analysis (1 propofol vs. 2 events for non-propofol). CONCLUSION: In this large colonoscopy cohort, propofol sedation was not associated with improved ADR, PDR, or cecal intubation rates.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".