Olfactory dysfunction is a risk factor for the comorbidity of mild cognitive impairment and Type 2 diabetes mellitus.
Bibliographic record
Abstract
OBJECTIVES: Diabetes can accelerate cognitive decline and hence affect the prognosis of patients with Type 2 diabetes mellitus (T2DM). Olfactory assessment can facilitate the early identification of cognitive impairment among T2DM patients. This study aims to evaluate the effects of olfactory function on mild cognitive impairment (MCI) in patients with T2DM. METHODS: test, and multivariable logistic regression was used to determine the relevant factors contributing to the comorbidity of MCI and T2DM. RESULTS: <0.05]. The number of patients with olfactory dysfunction also differed significantly between the 2 groups (120 vs 50). After adjustment for age, educational level, T2DM duration, fasting insulin, and glycosylated hemoglobin (HbA1c), multivariate logistic regression analysis showed older age (OR=1.14, 95% CI 1.09 to 1.20), longer course of diabetes (OR=1.21, 95% CI 1.12 to 1.31), and olfactory-impaired (OR=4.61, 95% CI 3.04 to 6.18) were independent risk factors for T2DM combined with MCI, and the high education level (OR=0.26, 95% CI 0.15 to 0.38) was an independent protective factor for T2DM combined with MCI. CONCLUSIONS: Olfactory dysfunction is an independent risk factor for the comorbidity of MCI and T2DM. Special attention should be paid to those with olfactory dysfunction when carrying out cognitive interventions in T2DM patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".