Rates of Minor Adverse Events and Health Resource Utilization Postcolonoscopy
Bibliographic record
Abstract
BACKGROUND: Little is known about minor adverse events (MAEs) following outpatient colonoscopies and associated health care resource utilization. OBJECTIVE: To estimate the rates of incident MAE at two, 14 and 30 days postcolonoscopy, and associated health care resource utilization. A secondary aim was to identify factors associated with cumulative 30-day MAE incidence. METHODS: A longitudinal cohort study was conducted among individuals undergoing an outpatient colonoscopy at the Montreal General Hospital (Montreal, Quebec). Before colonoscopy, consecutive individuals were enrolled and interviewed to obtain data regarding age, sex, comorbidities, use of antiplatelets/anticoagulants and previous symptoms. Endoscopy reports were reviewed for intracolonoscopy procedures (biopsy, polypectomy). Telephone or Internet follow-up was used to obtain data regarding MAEs (abdominal pain, bloating, diarrhea, constipation, nausea, vomiting, blood in the stools, rectal or anal pain, headaches, other) and health resource use (visits to emergency department, primary care doctor, gastroenterologist; consults with nurse, pharmacist or telephone hotline). Rates of incident MAEs and health resources utilization were estimated using Bayesian hierarchical modelling to account for patient clustering within physician practices. RESULTS: Of the 705 individuals approached, 420 (59.6%) were enrolled. Incident MAE rates at the two-, 14- and 30-day follow-ups were 17.3% (95% credible interval [CrI] 8.1% to 30%), 10.5% (95% CrI 2.9% to 23.7%) and 3.2% (95% CrI 0.01% to 19.8%), respectively. The 30-day rate of health resources utilization was 1.7%, with 0.95% of participants seeking the services of a physician. No predictors of the cumulative 30-day incidence of MAEs were identified. DISCUSSION: The incidence of MAEs was highest in the 48 h following colonoscopy and uncommon after two weeks, supporting the Canadian Association of Gastroenterology's recommendation for assessment of late complications at 14 days. Predictors of new onset of MAEs were not identified, but wide CrIs did not rule out possible associations. Although <1% of participants reported consulting a physician for MAEs, this figure may represent a substantial number of visits given the increasing number of colonoscopies performed annually. CONCLUSION: Postcolonoscopy MAEs are common, occur mainly in the first two weeks postcolonoscopy and result in little use of health resources.
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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 | 0.003 | 0.015 |
| 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 | 0.000 |
| 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".