969-P: Changes in Patterns of Care in Diabetes via Virtual Care Delivery Prior to and During COVID-Pandemic
Bibliographic record
Abstract
Background: The COVID-pandemic and virtual care has impacted the care delivery of many health care conditions including diabetes. This study aimed to compare how patterns of care for diabetes have changed as a result of virtual care and the COVID-pandemic at a large academic ambulatory care facility in Toronto, Canada. Methods: Patients were included who had an initial diabetes visit between September 15, 20 and September 20, 2020. Fisher's exact test was used to determine differences in care patterns between visits pre and during COVID- (after March 14, 2020) . Results: Pre-COVID-19, there were 240 (72.7%) completed initial visits and 90 (27.3%) follow-up visits, including 27.5% and 36.7% for type 1 diabetes, respectively. During COVID-19, there were 235 (44.1%) initial visits and 298 (56%) follow-up visits including 29.4% and 27.2% for type 1 diabetes, respectively. Including all visits, there were more no-shows during COVID-19, 4.5% vs. 1.5%; p<0.05. Out of 330 pre-COVID-visits, all (100%) were done in-person. During COVID-19, out of 533 visits, 89.3% were done by phone, 7.5% by video, and only 3.2% in-person. Conclusion: During COVID-19, there were more follow-up diabetes visits seen compared to initial visits and more no-shows. Most diabetes visits were done by phone during COVID-19. More data is needed to understand how diabetes care delivery has changed as a result of virtual care during COVID-19. Disclosure P.Palcu: None. G.Mukerji: n/a. C.Chu: None. C.Pendrith: None.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".