Cognitive impairment among patients with diabetes in Saudi Arabia: a cross-sectional study
Bibliographic record
Abstract
Abstract Background The incidence of cognitive impairment (CI) is higher in patients with diabetes mellitus (DM). The association between DM and CI and the risk factors for CI need to be addressed to estimate the prevalence of cognitive impairment in patients with DM and to identify the potential risk factors. The study is a cross-sectional study using a convenient sample of 269 subjects. Sociodemographic diabetes-related variables including biochemical markers were collected. CI and diabetes-related distress (DRD) were assessed using the Arabic version of Montreal Cognitive Assessment scale (MoCA) and the Diabetes Distress Screening Scale respectively. Results Overall, 80.3% had cognitive impairment while 33.8% had severe impairment. Older age, female gender, low level of education, and low income were associated with CI; duration of diabetes and DRD were associated with CI while ophthalmic complications were associated with severe CI. Duration of diabetes was inversely associated with CI. Level of HbA1c was significantly higher in patients with severe CI, and the probability of CI increased as the level of HbA1c increased. Low level of education was associated with severe CI, and CI was two times more likely among patients with DRD. Conclusion CI was higher than worldwide figures. Elderly females with low educational level, long duration of DM, and low socioeconomic status are at more risk. The probability of severe CI increased with increased level of HbA1c. Screening for CI in patients with diabetes along with intervention programs while considering the DRD and the level of HbA1c is crucial.
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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 | 0.001 |
| 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) | 0.001 | 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".