Community pharmacy-based A1c screening: a Canadian model for diabetes care
Bibliographic record
Abstract
OBJECTIVES: Point-of-care HbA1c screening devices are a valuable tool that community pharmacists can use to monitor patients with diabetes and improve their overall management. We previously reported our experiences using these devices to assess glycaemic control in diabetic patients at three community pharmacy locations in Toronto, Ontario. Here, we report data from screening of over 1000 patients at clinics held across Canada. METHODS: Community pharmacies across Canada offering A1c screening as part of their professional programmes were invited to upload screening data to a central database. A1c analysis was performed using the Bayer A1c Now. Patient recruitment and approach to A1c screening were at the discretion of the participating pharmacies and were not standardized. Data collection took place over a period of 8 months. KEY FINDINGS: The majority of patients screened (59.1%) had A1c values above target, indicating inadequate glycaemic control. Glycaemic control was generally poorer among patients on more intensive treatment regimens. A total of 1711 clinical interventions were performed by pharmacists. An average of two interventions were performed per patient, and we observed a trend towards increased numbers of interventions in patients with poorer glycaemic control. The prevalence of specific types of interventions showed an apparent shift from predominantly pharmacist-directed interventions in patients with better glycaemic control towards an increased prevalence of physician-directed interventions in patients with poorer glycaemic control. CONCLUSIONS: These results illustrate the prevalence of suboptimal glycaemic control among diabetic patients in the community, which represents a significant opportunity for pharmacists to use point-of-care screening to detect hyperglycaemia and intervene to improve disease management when warranted.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".