Cognitive impairment is associated with female sex, low level of education, lack of spousal relationship, and serum testosterone in type 2 diabetes
Bibliographic record
Abstract
Abstract Background: Relationship between cognitive impairment, serum testosterone, and body mass index in type 2 diabetes (T2D) is not fully understood. Objectives: To compare cognition between T2D patients and healthy controls. To determine relationship of mild cognitive impairment (MCI) with serum total testosterone and sociodemographic factors. Materials and Methods: The study was a cross-sectional case-control study. About 17 male and female T2D patients and healthy controls were randomly selected. Montreal cognitive assessment-basic (MoCA-B) was used to assess cognition; anthropometric indices were measured using standard protocols, while total testosterone was assayed from serum using competitive ELISA kits. Results: The mean age of the T2D patients and healthy controls was 51.62 and 66.76 years, respectively. About 82.35% of the T2D patients had MCI compared to 58.82% of the healthy controls. MCI was associated with female sex ( P = 0.033), lack of spousal relationship ( P = 0.016), low level of education ( P = 0.014), and normal or high level of total testosterone ( P = 0.010). Conclusion: Proportion of MCI in T2D patients is high and is associated with female sex, lack of spousal relationship, low level of education, and normal or high testosterone level.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".