Memory Impairments Associated With Postprandial Hyperglycemia and Glycemic Control
Bibliographic record
Abstract
We thank Kubiak et al. (1) for their thoughtful appraisal of our study relating to memory response in adults with type 2 diabetes following carbohydrate ingestion (2). We agree entirely with their comment that the underlying origins of memory impairment in this population is poorly understood and that lack of consensus on standardized neuropsychologic testing procedures may, in part, be contributing to this confusion (3). Clearly, as Kubiak et al. point out, a major contributor to the variance in cognitive performance observed in this population is the high prevalence of other risk factors for cognitive decline, including cardiovascular disease, hypertension, and depression (4,5), making it challenging to isolate the potential contribution of diabetes per se. We sought to explore cognitive function in adults with type 2 diabetes by perturbing the system through the administration of glucose, which is a treatment commonly used in studies of cognitive aging to explore the system’s plasticity in the face of underlying age-related deficits (6,7). A major advantage of applying this approach to the type 2 diabetic population is that changes observed in response to the challenge were unlikely to be directly attributable to vascular complications. Our data provided evidence for cognitive deficits, primarily related to declarative memory function, following the ingestion of 50 g of glucose in the form of …
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".