Continuous Glucose Monitoring in the Management of Congenital Hyperinsulinism: A National User-satisfaction Survey, UK
Bibliographic record
Abstract
CONTEXT: Congenital hyperinsulinism (CHI) causes severe and recurrent hypoglycemia with a 33% to 50% risk of neurodisability necessitating rigorous glucose monitoring. Continuous glucose monitoring (CGM) is now widely used in CHI with limited data on user-reported benefits. There are no validated instruments available to assess the impact of CGM use on quality of life (QOL) in CHI. OBJECTIVE: To evaluate CGM user satisfaction of patients with CHI and their carers. DESIGN: Modified CGM-satisfaction (CGM-SAT) and glucose monitoring surveys (GMS) were distributed electronically to CHI families using CGM. SETTING: CHI highly specialized services, UK, May to August 2023. PATIENTS OR OTHER PARTICIPANTS: Parents (n = 86) and teachers (n = 15) of patients with CHI using CGM (0-18 years old) and patients themselves if ≥7 years old (n = 20). MAIN OUTCOME MEASURES: User-reported ease of CGM handling, influence of CGM on CHI management and QOL, and desire for continued use of CGM. RESULTS: Most respondents reacted positively to statements regarding: CGM device handling (58% agreed or strongly agreed), influence on CHI management (70%), QOL (75%), and continued use (86%). Satisfaction with CGM was positively correlated with duration of use (r = 0.40, P < 0.001). Eighty-six percent of users reported checking the CGM 1 to 5 times per hour. Users reported perceived improvements in safety, hypoglycemia detection, freedom, and independence, despite concerns with accuracy and device range. CONCLUSION: Patients with CHI and their carers reported that they feel safer and perceive benefits from CGM in all aspects of living with CHI.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".