Virtual vs In-person Care in Gestational Diabetes Management: A Retrospective Cohort Analysis
Bibliographic record
Abstract
OBJECTIVES: The rising prevalence of gestational diabetes (GDM) presents a challenge to health-care systems. Virtual care has emerged as a potential solution to alleviate this burden, but limited data exist on its effectiveness. In this study, we evaluated maternal and neonatal outcomes in individuals with GDM managed with virtual care vs in-person care. METHODS: A retrospective cohort study was conducted among individuals with GDM attending interdisciplinary diabetes in pregnancy clinics in Calgary, Alberta, between 2017 and 2022. The primary exposure was modality of the initial visit (virtual or in-person) with a certified diabetes educator. Logistic regression models were used to analyze the relationship between visit modality and outcomes, adjusting for multiples, socioeconomic status, maternal age, infant sex, parity, and before vs during the severe acute respiratory syndrome coronavirus-2 (COVID-19) pandemic. RESULTS: Of the 9,511 individuals included, 4,236 had an initial virtual visit. Those in the virtual care group had lower odds of delivering large-for-gestational-age infants (adjusted odds ratio [aOR] 0.79, 95% confidence interval [CI] 0.65 to 0.97) and undergoing cesarean section (aOR 0.88, 95% CI 0.79 to 0.99). They also had lower odds of missing at least 1 appointment (aOR 0.89, 95% CI 0.77 to 0.99) and greater odds of being prescribed both insulin and metformin (aOR 1.30, 95% CI 1.16 to 1.46). No significant differences were found in rates of operative vaginal birth, induction of labour, small-for-gestational-age infants, 5-minute Apgar score <7, or neonatal intensive care unit admission. CONCLUSIONS: This study highlights the potential of virtual care to enhance GDM management. Further research is needed to assess its broader impact and optimize implementation strategies for diverse populations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".