A Population-based Analysis of Outpatient Colonoscopy in Adults Assisted by an Anesthesiologist
Bibliographic record
Abstract
BACKGROUND: The use of propofol to sedate patients for colonoscopy, generally administered by an anesthesiologist in North America, is increasingly popular. In the United States, regional use of anesthesiologist-assisted endoscopy appears to correlate with local payor policy. This study's objective was to identify nonpayor factors (patient, physician, institution) associated with anesthesiologist assistance at colonoscopy. METHODS: The authors performed a population-based cross-sectional analysis using Ontario health administrative data, 1993-2005. All outpatient colonoscopies performed on adults were identified. Hierarchical multivariable modeling was used to identify patient (age, sex, income quintile, comorbidity), physician (specialty, colonoscopy volume), and institution (type, volume) factors associated with receipt of anesthesiologist-assisted colonoscopy. RESULTS: During the study period, 1,838,879 colonoscopies were performed on 1,202,548 patients. The proportion of anesthesiologist-assisted colonoscopies rose from 8.4% in 1993 to 19.1% in 2005 (P < 0.0001). In the hierarchical model, patients in low-volume community hospitals were five times more likely to receive anesthesiologist-assisted colonoscopy than patients in high-volume community hospitals (odds ration 4.9; 95% confidence interval 4.4-5.5). Less than 1% of colonoscopies in academic hospitals were anesthesiologist-assisted. Compared to gastroenterologists, surgeons were more likely to perform anesthesiologist-associated colonoscopy (odds ratio 1.7; 95% confidence interval 1.1-2.6). CONCLUSIONS: In Ontario, rates of anesthesiologist-assisted colonoscopy have risen dramatically. Institution type was most strongly associated with this practice. Further investigation is needed to determine the most appropriate criteria for the use of anesthesiology services during colonoscopy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".