Are neurocognitive speed and inconsistency similarly affected in type 2 diabetes?
Bibliographic record
Abstract
Type 2 diabetes (T2D) is a disease of aging with indirect but detectable and cumulative neurological implications. We systematically tested whether neurocognitive speed (mean rate) or inconsistency (intraindividual variability) was the more sensitive clinical marker of T2D. Three of four research questions used a cross-sectional wave of the Victoria Longitudinal Study (VLS) divided into T2D (age 55-81 years) and control (age = 53-91 years) groups. The fourth question addressed relative two-wave longitudinal changes. Each of four speeded tasks produced intraindividual mean rate (IM) and intraindividual standard deviation (ISD) scores. First, the T2D group performed more slowly than the controls. Second, this deficit extended to inconsistency, but less uniformly. Third, based on logistic regression analyses, IM was the more effective predictor of T2D status. Fourth, we observed similar longitudinal change patterns for IM and ISD. Results are linked to the theoretical location of T2D on an adjusted neural vulnerability continuum.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".