872-P: Impact of Telemedicine Consultations during the COVID-19 Pandemic on Glycemic Control of Diabetes Patients
Bibliographic record
Abstract
Due to the COVID-19 pandemic, our endocrinology clinic transitioned to virtual care on March 18, 2020. The literature suggests that self-monitoring of blood glucose paired with telemedicine consultations is an effective strategy for diabetes management, however it is unclear whether telemedicine remains effective in the context of the sudden lifestyle changes caused by the pandemic, including non-essential service closures, travel restrictions, lockdowns, and psychosocial impacts. Patients with diabetes are uniquely affected by these restrictions, as glycemic control is heavily dependent on lifestyle and access to essential medications and supplies. The purpose of this project is to determine the impact of telemedicine consultations in the context of the COVID-19 pandemic on the glycemic control of patients seen at our clinic. A retrospective chart review was performed on 300 type 1 and 2 diabetes patients seen at least once within the 6 months preceding (pre-COVID) and following (post-COVID) March 18, 2020. The primary outcome measure was hemoglobin A1c. For patients with more than 1 A1c value in each time frame, the most recent A1c was used. Demographic information was also collected. There was no significant difference in the pre-COVID and post-COVID A1c values (p=0.40) of the entire sample. There was no significant difference in the pre-COVID and post-COVID A1c values when the sample was stratified by age, diabetes duration, use of CGM, or use of pump. However, there was a significant increase in A1c for females post-COVID (p<0.05). This difference was not observed for males (p=0.22). Our data suggests that telemedicine is an overall effective strategy for optimizing glycemic control of patients with type 1 and type 2 diabetes during the pandemic, with no significant difference in A1c. However, the gender-specific effect of telemedicine consultations during COVID-19 on the glycemic control of females with diabetes indicates a need for further study and intervention. Disclosure A. Dissanayake: None. M. Pawlowska: Advisory Panel; Self; Novo Nordisk. B. Schroeder: Advisory Panel; Self; AstraZeneca, Novartis Pharmaceuticals Canada Inc. J. Mackenzie-feder: None. A. White: Advisory Panel; Self; Abbott Diabetes, AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Novo Nordisk Canada Inc.
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 | 0.006 |
| 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 | 0.000 | 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".