Specialty-specific Evaluation of Virtual care Outcomes: A retrospective QUality and safety analysis (S-EVOQUe)
Bibliographic record
Abstract
The objective was to compare specialty-specific 7- and 30-day outcomes between virtual care visits and in-person visits which occurred during the SARS-CoV-2 pandemic. Using administrative data from provincial databases in Ontario, ambulatory care visits occurring virtually and in-person during specific timeframes within the pandemic were analyzed. Virtual care visits were matched with corresponding in-person visits based on multiple baseline patient characteristics. We assessed short-term patient outcomes at 7 and 30 days, including subsequent visits, hospital and ICU admissions, surgeries, and mortality and compared them using multivariate logistic regression. Odds ratios were calculated as measures of association between populations. For statistical significance, we used 99% confidence intervals to account for the increased likelihood of chance findings due to the multiple comparisons conducted. Overall, 9,340,519 visits were compared between populations using a 1:1 match on a 20% random sample of the available eligible visits. Over 70% of patients included were seen by a General Practitioner. With few exceptions and across almost all specialties, revisits, ED visits, admissions, ICU and OR use, and mortality were found to be more frequent for patients seen in person. When using the administrative data available to policy makers, there is no evidence to suggest that, in the short-term, virtual care is less safe than in person care. The causes for worse in-person outcomes are not yet clear although are likely related to the streaming of more acutely unwell patients towards in-person care.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".