Risk predictors of glycaemic control in children and adolescents with type 1 diabetes: A systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVES: To conduct systematic evaluation of the risk predictors of glycaemic control in children and adolescents with type 1 diabetes mellitus. METHODS: Cohort studies on risk predictors of glycaemic control in children and adolescents with type 1 diabetes were retrieved from CNKI, PubMed, Web of Science, Embase databases, etc. from the construction of the repository to 3 February 2023. Literature screening was conducted according to inclusion and exclusion criteria, then data extraction of region, sample size, age, follow-up time, risk predictors, outcome indicators, etc., and quality evaluation of The Newcastle-Ottawa Scale were conducted by two researchers while the third researcher makes decisions if there are disagreements. Finally, Revman5.4 and StataMP17 were used for meta-analysis. RESULTS: A total of 29 studies were included, and the results showed that insulin pump [Weighed mean difference (WMD) = -.48, 95% CI (-.73, -.24), p < .01], high-frequency sensor monitoring, early use of insulin pumps, prospective follow-up male, white race, large body mass index-standardised scoring, conscientiousness, agreeableness of mothers, eicosapentaenoic acid, leucine and protein (p < .05) were beneficial for reducing HbA1c levels in children and adolescents with diabetes. Ketoacidosis [WMD = .39, 95% CI (.28, .50), p < .01], selective admission, higher HbA1c level at one time (p < .01), higher glutamate decarboxylase antibody at 1 month after diagnosis, lower socio-economic status, non-living with biological parents, non-two-parent family, family disorder, family history of diabetes and high carbohydrate intake (p < .05) increased HbA1c levels in children and adolescents with diabetes. CONCLUSION: For children and adolescents with type 1 diabetes mellitus, the use of insulin pump, high-frequency sensor monitoring, prospective follow-up, good family support and reasonable diet are conducive to blood glucose control, while selective admission and DKA are not. Disease characteristics and demographic characteristics of children are closely related to subsequent blood glucose control, and the relationship between diagnosis age and blood glucose control needs to be further explored.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".