Variation in Access to Specialist Care and Risk of Surgery in Patients with Inflammatory Bowel Disease: A Population-Based Cohort Study
Bibliographic record
Abstract
IntroductionInflammatory bowel disease (IBD; subtypes: Crohn’s disease (CD) and ulcerative colitis (UC)) is a chronic disease of the gastrointestinal tract with rising prevalence among people ≥65y. Rural residents, especially those ≥65y, have decreased access to specialist care. Specialist care is associated with lower risk of hospitalization and surgery. Objectives and ApproachWe evaluated variation across physician networks in access to specialist care and surgery among incident patients ≥65y in Ontario health administrative data. Access to specialist care was defined as: ≥1 outpatient visit to gastroenterologists or the majority of IBD-specific outpatient care by gastroenterologists. Variation was assessed with multilevel logistic regression and median odds ratios (MOR), adjusting for age, sex, distance from IBD physician, comorbidities, neighbourhood income, and rural/urban. Models evaluating surgical risk also adjusted for specialist care use, emergency department visits, and hospitalization at diagnosis. Network-level variables included rurality (RIO score), population colonoscopy and gastroenterologist supply. ResultsThere was significant variation in having ≥1 gastroenterologist visit (CD p=0.0001, MOR 3.3; UC p<0.0001, MOR 3.1) and gastroenterologist providing the majority of care (CD p=0.0001, MOR 3.0; UC p<0.0001, MOR 3.7) within 12 months of diagnosis. Variation remained significant after accounting for network-level characteristics (≥1 gastroenterologist visit: CD p=0.0002, MOR 2.6, UC p<0.0001, MOR 2.2; majority of care: CD p=0.0002, MOR 2.4; UC p<0.0001, MOR 2.4). In CD, there was no variation in the five-year risk of surgery (p=0.07, MOR 1.3) and was unchanged by network-level factors (p=0.13, MOR 1.3). Variation in the risk of colectomy exists for patients with UC (p=0.016, MOR 1.3) and was not reduced when accounting for network-level characteristics (p=0.019, MOR 1.3). Conclusion/ImplicationsAccess to specialist care among patients with elderly-onset IBD is varies greatly between networks but this variation cannot be explained by differing provision of gastroenterological services across physician networks. Further research is needed to understand the factors that influence access to care and outcomes in elderly patients with IBD.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".