S196 The Impact of Socioeconomic and Demographic Factors on Treatment Outcomes in Patients With Pancreatic Fluid Collections
Bibliographic record
Abstract
Introduction: Pancreatic fluid collections (PFCs) cause significant morbidity and contribute to healthcare burden. While minimally invasive drainage improves outcomes, the influence of social determinants of health (SDOH) on access, treatment, and outcomes remains underexplored. We aimed to evaluate associations between SDOH and long-term outcomes in patients undergoing PFC drainage at a tertiary care center. Methods: We conducted a retrospective cohort study of consecutive adults who underwent PFC drainage at St. Michael’s Hospital, Toronto, from January 2016 to February 2024. Patient postal codes were linked to 2021 Canadian Census data to estimate income, housing, education, language, and Indigenous identity. Outcomes included clinical improvement (defined as the resolution of the drainage indication), radiologic resolution, and hospital readmission. Bivariate and multivariable logistic regression analyses were performed to assess associations. Results: Among 193 patients (median age 53.9 years, 63.7% men), 91.1% underwent initial endoscopic drainage, most commonly for infection (27.0%). Bivariate analysis revealed that Indigenous identity was associated with greater use of percutaneous drainage (P = 0.026), as well as higher rates of abdominal pain (P = 0.010), opioid (P = 0.001), and antibiotic prescriptions (P = 0.017) at discharge. Substance use disorder (SUD) was associated with alcohol-related pancreatitis (P < 0.001), pain at discharge (P = 0.022), prolonged PFC duration (P = 0.005), and higher readmission rates (P = 0.012). Lower median income was associated with lower rates of additional endoscopic drainage (P = 0.043). In multivariable models, Indigenous identity (odds ratio [OR] 0.864 [0.779–0.958], P = 0.005), shelter cost (OR 0.251 [0.093–0.671], P = 0.006), and post-secondary education (OR 0.938 [0.892–0.987], P = 0.014) were associated with lower odds of clinical improvement. Higher income (OR 1.100 [1.019–1.189], P = 0.015) and spending >30% of income on shelter (OR 1.124 [1.057–1.195], P < 0.001) predicted greater improvement. SUD was linked to reduced odds of radiologic resolution (OR 0.436 [0.183–0.999], P = 0.049). The income-to-shelter cost ratio predicted readmission (OR 1.031 [1.004–1.060], P = 0.028). Conclusion: SDOH, including Indigenous identity, income, shelter cost, and SUD, were associated with disparities in the treatment and outcomes of PFC drainage. These findings support the integration of social risk screening and equity-focused care pathways to improve outcomes among vulnerable populations.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".