A240 SOCIOECONOMIC STATUS AS A RISK FACTOR FOR ERCP OUTCOMES: A RETROSPECTIVE COHORT STUDY
Bibliographic record
Abstract
Abstract Background Endoscopic Retrograde Cholangiopancreatography (ERCP) has one of the highest adverse event rates among endoscopic procedures, close to 10%, including pancreatitis, bleeding, and perforation. To minimize complications and improve procedure success, understanding risk factors of adverse events is critical. Socioeconomic variables such as income, housing and education can influence health outcomes through access to resources, however, there is a literature gap regarding the effect of these variables on ERCP outcomes. Aims The purpose of this study is to investigate socioeconomic predictors of ERCP outcomes. Methods This study analyzed 6105 patients (51.4% female) who underwent ERCP at a tertiary care center between January 1, 2011 and December 31, 2020. Patient postal codes were translated to 2016 census division unique identifier codes which linked them to median monthly housing costs, household income, education levels, considered proxies for socioeconomic status. Multivariable logistic regression models were built to identify predictors of procedure success (achieving intended diagnostic or therapeutic goals) and adverse events (perforation, abdominal pain, pancreatitis, delayed/immediate bleeding). Data was analyzed using the “glm” package on R studio (Version 2023.06.1) and the adjusted P-value < 0.05 was considered for statistical significance. Results The multivariable logistic regression model demonstrated that better access to housing (OR:1.11 [1.01, 1.12], p=0.02) is associated with procedure success. Conversely, older age (OR: 0.86 [0.78, 0.93], p<0.01) and male sex (OR=0.81 [0.69, 0.95], p<0.0001) are associated with lower odds of procedure success. Higher housing costs (OR: 0.86 [0.75, 0.97], p=0.04) are associated with decreased adverse events, after adjusting for age and sex. Conclusions The findings of this study suggest socioeconomic differences may influence ERCP outcomes and should be further investigated in prospective studies. Funding Agencies None
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".