Access to Specialists and Emergency Department Visits in Inflammatory Bowel Disease: A Population-Based Study
Bibliographic record
Abstract
BACKGROUND AND AIMS: The number of inflammatory bowel disease [IBD]-related visits to the emergency department [ED] is increasing in North America. This study evaluates the relationship between access to specialists and utilization of ED services. METHODS: We conducted a population-based study of all IBD patients in Ontario in 2014-2015 to measure utilization of non-emergency IBD care by specialists [NICS] and ED visits. After characterizing regional variation in access to gastroenterologists and region-wide implementation of NICS, we constructed regression models to determine whether they were predictors of individual utilization of NICS and ED services. RESULTS: The number of gastroenterologists per 1000 IBD patients varied geographically, ranging from 1.13 to 10.65, as did the region-wide proportion of patients who received NICS, ranging from 21% to 52%. Compared with those with low access to gastroenterologists, those living in areas with moderate (odds ratio [OR], 2.37; 95% confidence interval [CI]: 2.27-2.47) and high [OR, 1.83; 95% CI: 1.71-1.95] access were more likely to receive NICS. The risk of visits to the ED was lower among those residing in regions with moderate [OR, 0.78; 95% CI: 0.75-0.82] and high access [OR, 0.74; 95% CI: 0.69-0.80] to gastroenterologists and in regions where implementation of NICS was not low [OR, 0.78; 95% CI: 0.75-0.81]. CONCLUSIONS: Poor access to outpatient IBD specialist care contributes to IBD-related ED visits. Strategies to increase specialist access may reduce the utilization of emergency services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".