Risk of adverse events associated with upper and lower endoscopic ultrasound: a population-based cohort study
Bibliographic record
Abstract
Abstract Background and study aim Endoscopic ultrasound (EUS) enables diagnostic evaluation and therapeutic interventions but is associated with adverse events. We conducted a population-based cohort study to determine the risk of adverse events for upper and lower EUS with and without fine-needle aspiration (FNA). Patients and methods All adults who underwent EUS and resided in Calgary in 2007–2013 were included. Endoscopy and provincial databases were used to identify EUS procedures, unplanned emergency department visits, and hospital admissions within 30 days of the procedures, which were then characterized through formal chart review. Adverse events were defined a priori and classified as definitely, possibly, or not related to EUS. The primary outcome was 30-day risk of adverse events classified as definitely or possibly related to EUS. Univariable and multivariable analyses were conducted with risk factors known to be associated with EUS adverse events. Results 2895 patients underwent 3552 EUS procedures: 3034 (85 %) upper EUS, of which 710 (23 %) included FNA, and 518 (15 %) lower EUS, of which 23 (4 %) involved FNA. Overall, 69 procedures (2 %) involved an adverse event that was either definitely or possibly related to EUS, with 33 (1 %) requiring hospitalization. None of the adverse events required intensive care or resulted in death. On multivariable analysis, only FNA was associated with increased risk of adverse events (odds ratio 6.43, 95 % confidence interval 3.92–10.55; P < 0.001). Conclusion Upper and lower EUS were generally safe but FNA substantially increased the risk of adverse events. EUS-related complications requiring hospitalization were rare.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".