The efficacy and safety of insulin pump therapy with predictive low glucose suspend feature in decreasing hypoglycemia in children with type 1 diabetes mellitus: A systematic review and meta‐analysis
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Bibliographic record
Abstract
BACKGROUND: Automated insulin delivery with predictive low glucose suspend (PLGS) feature has the potential to reduce risk of hypoglycemia in patients with type 1 diabetes mellitus (T1DM). We aim to systematically synthesize the evidence on the efficacy and safety of PLGS in children and adolescents with T1DM. METHODS: We performed a systematic search through Ovid/MEDLINE, Ovid/Embase, and other search engines. We included randomized controlled trials (RCTs) evaluating the effect of sensor augmented pump (SAP) with PLGS feature compared to SAP or insulin pump therapy without SAP in decreasing hypoglycemia in children and adolescents aged 2 to 18 years with T1DM, with at least 2 weeks of follow-up. Two reviewers independently selected studies, extracted data, and evaluated the risk of bias (ROB). RESULTS: Five RCTs with total sample size of 493 children aged 6 to 18 years met the inclusion criteria. The overall ROB of included studies was low. There is high quality evidence that PLGS is superior to SAP in decreasing time spent in hypoglycemia (sensor glucose [SG] <3.9 mmol/L [<70 mg/dL]/24 h) and nocturnal hypoglycemia (SG <3.9 mmol [<70 mg/dL]/L/night) with an absolute mean difference of 17.4 min/d (95% CI: -19.2, -15.5) and 26.3 min/night (95% CI: -35.5, -16.7), respectively, without increasing percentage of time spent in hyperglycemia or episodes of diabetic ketoacidosis (DKA). There was insufficient evidence for the impact of PLGS on health related quality of life (HRQL). CONCLUSIONS: PLGS is superior to SAP in decreasing daytime and nocturnal hypoglycemia without increasing the risk of DKA or hyperglycemia. Future studies should address the impact of PLGS on children younger than 6-years-old and HRQL.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it