Patterns of specialist healthcare delivery among inflammatory bowel disease patients in response to the COVID-19 pandemic in Ontario: a population-based study
Bibliographic record
Abstract
Background: Access to inflammatory bowel diseases (IBD) specialist care is a predictor of health outcomes. We sought to characterize the impact of the pandemic on patterns of IBD healthcare delivery and whether it compromised overall access to care. Methods: We identified adults with an IBD diagnosis residing in Ontario between 2016 and 2021 using administrative data at ICES. We determined quarterly rates of in-person and virtual IBD specialist visits and stratified that by regions with high and low access to IBD specialists. We stratified our analyses into 3 periods: pre-COVID, immediate COVID, and maintenance COVID. We performed interrupted time series analysis to assess for time trends. Results: During the immediate COVID phase, there was a 69% relative quarterly decline in in-person IBD specialist visits with a concurrent 591% relative quarterly rise in rates of virtual visits. Entering the COVID maintenance phase, there was a 7% quarterly relative decline in the rate of in-person visits, and a 7% and 4% quarterly relative increases in the rates of virtual and total IBD specialist visits, respectively. Pre-pandemic, IBD patients residing in regions with high specialist access had a 16% higher rate of visits than those in low-access regions. During the COVID maintenance phase, the disparity was reduced to 12%. Conclusions: During the COVID-19 pandemic, the rapid transition from in-person to virtual IBD specialist care led to a slight increase in overall IBD visits. There was also a small decrease in the gap in rates of IBD specialist visits between high- and low-access regions.
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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 | 0.003 |
| 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".