Glycemic extremes are related to cognitive dysfunction in children with type 1 diabetes: A meta‐analysis
Bibliographic record
Abstract
AIMS/INTRODUCTION: To examine the magnitude and pattern of cognitive dysfunction in children with type 1 diabetes, and the possible effects associated with other disease variables, such as early onset diabetes, severe hypoglycemia and hyperglycemia. MATERIALS AND METHODS: We carried out a meta-analysis using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. We searched MedLine, Embase and PsycINFO to identify studies on cognitive function in children with type 1 diabetes that were published up until 30 September 2016. Effect sizes understood as the standardized mean differences between groups with diabetes and control groups (i.e., Hedges' g) were calculated to quantify the extent of cognitive dysfunction in those groups consisting of children with diabetes. RESULTS: A total of 19 studies met our inclusion criteria, comprising 1,355 participants with type 1 diabetes and 696 controls. Compared with non-diabetic controls, children with type 1 diabetes showed a significantly poorer cognitive performance overall (g = -0.46), as well as specific deficits in full-scale intelligence (g = -1.06), attention (g = -0.60) and psychomotor speed (g = -0.46). Glycemic extremes were associated with poorer overall cognition (g = -0.18), as well as slightly lower performance in memory (g = -0.27). CONCLUSIONS: We found that type 1 diabetes was associated with cognitive dysfunction characterized by a lowered intelligence, diminished attention and a slowing of psychomotor speed. Glycemic extremes, which are described as a period of high glucose levels and severe hypoglycemia, were related to cognitive dysfunction in children with type 1 diabetes.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.041 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".