Continuous Monitoring of Glucose for Type 1 Diabetes: A Health Technology Assessment.
Bibliographic record
Abstract
BACKGROUND: Type 1 diabetes is a condition in which the pancreas produces little or no insulin. People with type 1 diabetes must manage their blood glucose levels by monitoring the amount of glucose in their blood and administering appropriate amounts of insulin via injection or an insulin pump. Continuous glucose monitoring may be beneficial compared to self-monitoring of blood glucose using a blood glucose meter. It provides insight into a person's blood glucose levels on a continuous basis, and can identify whether blood glucose levels are trending up or down. METHODS: We conducted a health technology assessment, which included an evaluation of clinical benefit, value for money, and patient preferences related to continuous glucose monitoring. We compared continuous glucose monitoring with self-monitoring of blood glucose using a finger-prick and a blood glucose meter. We performed a systematic literature search for studies published since January 1, 2010. We created a Markov model projecting the lifetime horizon of adults with type 1 diabetes, and performed a budget impact analysis from the perspective of the health care payer. We also conducted interviews and focus group discussions with people who self-manage their type 1 diabetes or support the management of a child with type 1 diabetes. RESULTS: Twenty studies were included in the clinical evidence review. Compared with self-monitoring of blood glucose, continuous glucose monitoring improved the percentage of time patients spent in the target glycemic range by 9.6% (95% confidence interval 8.0-11.2) to 10.0% (95% confidence interval 6.75-13.25) and decreased the number of severe hypoglycemic events.Continuous glucose monitoring was associated with higher costs and small increases in health benefits (quality-adjusted life-years). Incremental cost-effectiveness ratios (ICERs) ranged from $592,206 to $1,108,812 per quality-adjusted life-year gained in analyses comparing four continuous glucose monitoring interventions to usual care. However, the uncertainty around the ICERs was large. The net budget impact of publicly funding continuous glucose monitoring assuming a 20% annual increase in adoption of continuous glucose monitoring would range from $8.5 million in year 1 to $16.2 million in year 5.Patient engagement surrounding the topic of continuous glucose monitoring was robust. Patients perceived that these devices provided important social, emotional, and medical and safety benefits in managing type 1 diabetes, especially in children. CONCLUSIONS: Continuous glucose monitoring was more effective than self-monitoring of blood glucose in managing type 1 diabetes for some outcomes, such as time spent in the target glucose range and time spent outside the target glucose range (moderate certainty in this evidence). We were less certain that continuous glucose monitoring would reduce the number of severe hypoglycemic events. Compared with self-monitoring of blood glucose, the costs of continuous glucose monitoring were higher, with only small increases in health benefits. Publicly funding continuous glucose monitoring for the type 1 diabetes population in Ontario would result in additional costs to the health system over the next 5 years. Adult patients and parents of children with type 1 diabetes reported very positive experiences with continuous glucose monitoring. The high ongoing cost of continuous glucose monitoring devices was seen as the greatest barrier to their widespread use.
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".