A24 RATE OF GASTROINTESTINAL ENDOSCOPY USE AND CONCOMITTANT ANESHESIOLOGY ASSISTANCE AND PREDICTORS OF ANESTHESIOLOGY ASSISTANCE: A POPULATION-BASED ANALYSIS
Bibliographic record
Abstract
Abstract Background There are limited Canadian data on time trends of gastrointestinal endoscopy (GIE) use and concomitant anesthesiology assistance. Aims To test the time trends and variations in concomitant anesthesiology assistance during GIE in the central Canadian province of Manitoba. Methods Physicians claims reimbursement data from the provincial health ministry were used to ascertain upper & lower GIE procedures and anesthesiology assistance annually from 1984 to 2016. The annual provincial population was determined from health insurance registration files. Generalized linear models with generalized estimating equations were used to test the linear trend over time and differences amongst age groups, health regions and income quintiles. Logistic regression analysis with generalized estimating equations was used to assess predictors of anesthesiology use. Results 410, 685 individuals had at least one procedure in the observation period. Approximately half of the procedures were performed outside the recommended CRC screening age group of 50–74 years, with 38% among those less than 50 years. The average annual rate of increase was 4.6% (95% CI 4.4–4.8%). The rate of increase stabilized among the > 75 years group over the last 5 years of the study. There were marked regional variation in GIE procedures, with the smallest increase in Winnipeg region (the largest urban region):2.9% (95%CI: 2.5–3.3) vs. 5.2% (95%CI: 4.8–5.6) in Southern Health; RR: 2.2(figure 1 A). There were no differences in GIE rates by income quintile. Similar patterns were seen in analysis stratified by upper and lower GIE. Concomitant anesthesiology use ranged from 1% in Winnipeg region to 80% in the southern rural region (figure 1B). In the logistic regression analysis, independent predictors of concomitant anesthesiology use in rural regions included younger age, female sex, lower income quintile, and higher comorbidities of patients, GP endoscopist, region of physician practice, and lower volume endoscopy physician. Conclusions Marked regional variations in GIE use and concomitant anesthesiology use were observed in a universal health care system. Efforts to standardize care are needed to reduce variations. Funding Agencies Manitoba Health
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".