Study of MiniMed 640G Insulin Pump with SmartGuard in Prevention of Low Glucose Events in Adults with Type 1 Diabetes (SMILE): Design of a Hypoglycemia Prevention Trial with Continuous Glucose Monitoring Data as Outcomes
Bibliographic record
Abstract
BACKGROUND: Sensor-integrated pump systems with low-glucose suspend (also known as threshold suspend) functions have markedly transformed the management of type 1 diabetes, but most studies to date have excluded patients at high risk of hypoglycemia. The SMILE study is investigating the efficacy of the MiniMed™ 640G insulin pump with the SmartGuard™ predictive low-glucose management (PLGM) feature in the prevention of hypoglycemia in adults with type 1 diabetes, who are at high risk of hypoglycemia. METHODS: SMILE is a prospective, randomized, open-label, controlled trial being undertaken in four European countries and Canada. Following a 2-week run-in phase, eligible participants will be randomized to use either the MiniMed 640G system with continuous glucose monitoring (CGM) and the SmartGuard PLGM feature on continuously for 24 weeks (treatment arm), or the MiniMed 640G without CGM and with blinded continuous glucose measurements between weeks 10-12, 16-18, and 22-24 (control arm). The primary endpoint is the mean number of hypoglycemic events, defined as sensor glucose ≤55 mg/dL (≤3.0 mmol/L) for >20 consecutive minutes. Secondary endpoints include various glycemic indices in the hypoglycemic and hyperglycemic ranges, as well as glycated hemoglobin. Data on patient-reported outcomes such as hypoglycemia awareness and treatment satisfaction will also be collected. CONCLUSIONS: It is anticipated that the SMILE study will provide important insights into the effectiveness of SmartGuard technology in adult patients with type 1 diabetes.
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 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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".