123-LB: Implementing a Multidisciplinary Model of CGM Care in Real-World Pharmacy Practice—A Clinical Consensus for Canadian Pharmacists
Bibliographic record
Abstract
Background: Continuous glucose monitoring (CGM) devices have transformed diabetes care, but there is a lack of guidance for pharmacists to follow when onboarding patients to begin using CGM systems. Our study aimed to develop a blueprint for best practices for pharmacists to support people living with diabetes (PWD) using CGM systems. Methods: We used a modified DELPHI process to gather insights from 11 Key Opinion Leaders (KOLs) across Canada, including two endocrinologists, one family physician, and eight pharmacists. Consensus was reached for each criterion when 75% agreement was achieved. Criteria for CGM device initiation, short-term or episodic CGM use, and best practices for CGM onboarding and monitoring were developed based on the results. Results: Amongst the ideal candidates for CGM use are PWD using insulin, those with type 1 diabetes, individuals not reaching their A1C target, those experiencing frequent hypoglycemia, pregnant individuals, and others at risk of hypoglycemia. Best practices for onboarding and monitoring included shared decision-making conversations, a comprehensive overview of cost and coverage options, customized alerts and alarms, and timely follow-up sessions. Discussion: The KOL group ensured a comprehensive and diverse perspective. The developed blueprint provides a valuable resource for pharmacists to improve the use of CGM devices and enhance diabetes management for their patients. Incorporating these best practices into clinical practice has the potential to transform diabetes care and improve outcomes. Disclosure A. S. Sihota: Consultant; Novo Nordisk Canada Inc., Other Relationship; Becton, Dickinson and Company. S. Sivapalan: Advisory Panel; Novo Nordisk, Pfizer Inc., AstraZeneca, Consultant; Pear Healthcare Solutions Inc., Other Relationship; Boehringer Ingelheim (Canada) Ltd., GlaxoSmithKline plc., Novo Nordisk, Bristol-Myers Squibb Company, Pfizer Inc., AbbVie Inc., Ferring Pharmaceuticals, AstraZeneca. T. Smith: Advisory Panel; Novo Nordisk Canada Inc., Dexcom, Inc., Emergent Biosolutions. I. Halperin: Advisory Panel; Sanofi, Speaker's Bureau; 3Boehringer Ingelheim Canada Ltd. /Ltée, Abbott Diabetes, Dexcom, Inc., Novo Nordisk. A. B. Jain: Advisory Panel; Abbott, Amgen Canada, Dexcom, Inc., AstraZeneca, Novo Nordisk, Bayer Inc., Insulet Corporation, Takeda Canada, Boehringer Ingelheim (Canada) Ltd., Eli Lilly and Company, Research Support; Abbott, Amgen Canada, Novo Nordisk, Speaker's Bureau; Abbott, Amgen Canada, Dexcom, Inc., AstraZeneca, Novo Nordisk, Bausch Health, Canada, Boehringer Ingelheim (Canada) Ltd., Eli Lilly and Company, Pfizer Inc. A. Chin: Other Relationship; Abbott Diabetes, BMO Bank of Montreal, Diabetes Canada, Dexcom, Inc., Roche Diabetes Care, Tandem Diabetes Care, Inc., Research Support; Novavax, Centricity Research. W. Chow: Advisory Panel; GlaxoSmithKline plc., Pfizer Inc., Speaker's Bureau; Abbott Diagnostics, AstraZeneca, Bayer Inc., Bausch Health, Canada, Boehringer Ingelheim (Canada) Ltd., Eisai Inc., Novo Nordisk. S. Jin: Advisory Panel; Dexcom, Inc., Eisai Inc., Novo Nordisk Canada Inc., Board Member; Wounds Canada, Consultant; Abbott Diabetes, Boehringer Ingelheim Pharmaceuticals Inc., Canadian Collaborative Research Network, Diabetes Canada, GlaxoSmithKline plc., HLS Therapeutics Inc., MDBriefcase, Novo Nordisk Canada Inc., AstraZeneca, Speaker's Bureau; Eisai Inc., Dexcom, Inc., Novo Nordisk Canada Inc. T. Molberg: None. S. Patil: None. R. Siemens: Consultant; Canadian Collaborative Research Network, Novartis Canada, AstraZeneca, Montmed, Sanofi, MDBriefcase, Other Relationship; Abbott Diabetes, Amgen Canada, Boehringer Ingelheim and Eli Lilly Alliance, GlaxoSmithKline plc., HLS Therapeutics, Novo Nordisk, Dexcom Canada, Diabetes Canada. Funding Dexcom Canada
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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 | 0.059 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| 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".