Impact of Diabetes and Its Treatment on Cognitive Function Among Adolescents Who Participated in the Diabetes Control and Complications Trial
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to evaluate whether severe hypoglycemia or intensive therapy affects cognitive performance over time in a subgroup of patients who were aged 13-19 years at entry in the Diabetes Control and Complications Trial (DCCT). RESEARCH DESIGN AND METHODS: This was a longitudinal study involving 249 patients with type 1 diabetes who were between 13 and 19 years old when they were randomly assigned in the DCCT. Scores on a comprehensive battery of cognitive tests obtained during the Epidemiology of Diabetes Interventions and Complications follow-up study, approximately 18 years later, were compared with baseline performance. We assessed the effects of the original DCCT treatment group assignment, mean A1C values, and frequency of severe hypoglycemic events on eight domains of cognition. RESULTS: There were a total of 294 reported episodes of coma or seizure. Neither frequency of hypoglycemia nor previous treatment group was associated with decline on any cognitive domain. As in a previous analysis of the entire study cohort, higher A1C values were associated with declines in the psychomotor and mental efficiency domain (P < 0.01); however, the previous finding of improved motor speed with lower A1C values was not replicated in this subgroup analysis. CONCLUSIONS: Despite relatively high rates of severe hypoglycemia, cognitive function did not decline over an extended period of time in the youngest cohort of patients 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".