Propofol sedation does not improve measures of colonoscopy quality but increase cost – findings from a large population-based cohort study
Bibliographic record
Abstract
Background: Propofol is often used for sedation during colonoscopy. We assessed the impact of propofol sedation on colonoscopy related quality metrics and cost in a population-based cohort study. Methods: All colonoscopies performed at 21 hospitals in the province of Ontario, Canada, during an 18-month period, from April 1, 2017 to October 31, 2018, using either propofol or conscious sedation were evaluated. The primary outcome was adenoma detection rate (ADR) and secondary outcomes were sessile serrated polyp detection rate (ssPDR), polyp detection rate (PDR), cecal intubation rate (CIR), and perforation rate. Binary outcomes were assessed using a modified Poisson regression model adjusted for clustering and potential confounders based on patient, procedure, and physician characteristics. Findings: A total of 46,634 colonoscopies were performed, of which 16,408 (35.2%) received propofol and 30,226 (64.8%) received conscious sedation. Compared to conscious sedation, the use of propofol was associated with a lower ADR (24.6% vs. 27.0%, p < 0.0001) but not ssPDR (5.0% vs. 4.7%, p = 0.26), PDR (40.5% vs 40.4%, p = 0.79), CIR (97.1% vs. 96.8%, p = 0.15) or perforation rate (0.04% vs. 0.06%, p = 0.45). On multi-variable analysis, propofol sedation was not associated with any differences in ADR (RR = 0.90, 95% CI 0.74-1.10, p = 0.30), ssPDR (RR = 1.20, 95% CI 0.90-1.60, p = 0.22), PDR (RR = 1.00, 95% CI 0.90-1.11, p = 0.99), or CIR (RR = 1.00, 95% CI 0.80-1.26, p = 0.99). The additional cost associated with propofol sedation was $12,730,496 for every 100,000 cases. Interpretation: The use of propofol sedation was not associated with improved colonoscopy related quality metrics but increased costs. The routine use of propofol for colonoscopy should be reevaluated. Funding: None.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".