Sensor-Based Technology: Bringing Value to People with Diabetes and the Healthcare System in an Evolving World
Bibliographic record
Abstract
PURPOSE: Evidence demonstrates that glucose-sensing technologies have enabled effective glycemic control for adults and children with type 1 diabetes (T1DM) or adults with type 2 diabetes (T2DM) on insulin therapy or non-insulin therapy. Here, we report on the wider value of glucose-sensing technology from the perspectives of person living with diabetes (PWD), healthcare providers (HCPs), and healthcare policy stakeholders. METHODOLOGY: flash glucose monitoring system in diabetes. These findings were combined with the outcomes of three healthcare attitudes surveys among PWD and diabetes healthcare professionals in Canada, including two commissioned for this purpose. RESULTS: Clinical trials data and real-world evidence have proven the benefits of the FreeStyle Libre system on limiting hypoglycemia, lowering HbA1c, optimizing metrics of glucose control and reducing hospital admissions. These benefits are accompanied by improvements in patients' quality of life, work productivity, and savings to the health system. The FreeStyle Libre system has created an opportunity to change the organization and delivery of care, including during COVID-19 restrictions on access to standard care, thus generating system-wide benefits in addition to those accrued by patients and HCPs. CONCLUSION: Evidence-based improvements in glucose control for PWD using flash glucose monitoring are accompanied by increased treatment satisfaction and quality of life. Telemedicine with such remote monitoring systems increases the opportunities for simultaneous review of glucose data with HCPs and shared decision-making, thus encouraging adherence with treatment.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".