Variation in care of patients with elderly-onset inflammatory bowel disease in Ontario, Canada: A population-based cohort study
Bibliographic record
Abstract
Abstract Background Variation in health care, when not based on patient preference, may result in poorer care. We determined whether variation in health services utilization, gastroenterologist care and outcomes existed among patients with elderly-onset inflammatory bowel disease (IBD). Methods Patients with IBD (diagnosed ≥65 years) were identified from population-based health administrative data from Ontario, Canada (1999 to 2014). We assessed variation across multispecialty physician networks in gastroenterologist care and outcomes using multilevel logistic regression. Median odds ratios (MOR) described variation. We evaluated the association between gastroenterologist supply, specialist care and outcomes. Results In 4806 patients, there was significant variation in having ever seen a gastroenterologist (MOR 3.35, P < 0.0001), having a gastroenterologist as the primary IBD care provider (MOR 4.16, P < 0.0001), 5-year colectomy risk in ulcerative colitis (MOR 1.38, P = 0.01), immunomodulator use (MOR 1.47, P = 0.001), and corticosteroid use (MOR 1.26, P = 0.006). No variation in emergency department visits, hospitalizations or intestinal resection (Crohn’s) was noted. Patients in networks with fewer gastroenterologists were less likely to see a gastroenterologist (odds ratio [OR] 0.29, 95% confidence interval [CI] 0.15 to 0.56), have a gastroenterologist as their primary care provider (OR 0.27, 95% CI 0.12 to 0.59), be hospitalized within 5 years (OR 0.82, 95% CI 0.69 to 0.98), and be prescribed biologics within 1 year (OR 0.50, 95% CI 0.28 to 0.89). Conclusions Utilization of gastroenterology care in patients with elderly-onset IBD varies greatly. Patients treated by gastroenterologists and in networks with more gastroenterologists have better outcomes. There is a need to ensure all individuals with IBD have equal access to and utilization of specialist care to ensure the best possible outcomes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".