The Establishment of a Registry of Elderly Patients with Diabetes and an Investigation of the Inhibitory Factor of Cognitive Dysfunction
Bibliographic record
Abstract
Objectives: Diabetes and dementia has been considered to have pathological relevance. Early diagnosis and treatment for Mild Cognitive Impairment (MCI) are important to stop it from advancing to dementia. We investigated the rate of MCI or dementia, correlation between cognitive dysfunction (CD) and background factor in diabetes patients, a valuable marker for diagnosis and effective diabetes treatment for prevention of CD. Approach and Results: We identified 161 patients aged ≥60 years, outpatient treatment at Kyushu University hospital from 2016/7 ∼ 2017/11. The Japanese versions of Montreal Cognitive Assessment (MoCA-J) and Mini-Mental State Examination (MMSE) were performed as screening. The patients who showed MMSE≤27 or MoCA-J≤25 had head MRI/SPECT, visited dementia specialist and were diagnosed with the disease type. The CD, screening results, diabetic clinical findings, contents of treatment and relative factors were analyzed by univariate or multivariate analysis. The subjects of investigation were aged 72.6±7.4 years, female 74/161 (46%), BMI 23.3±3.4, HbA1c 7.1±0.8%, duration14.4±10.6 years, insulin therapy 49/161(30.8%). Screening Results: MMSE{MCI22(13.7%), dementia1(0.6%)}/{ MoCA-J: MCI 44 (27.3%), dementia 6 (3.7%)}. 22 patients visited a dementia specialist, and diagnosed with Alzheimer’s dementia 2, MCI 15 and normal 5. The sensitivity of MCI in MMSE, MoCA-J were 46.7% and 93.3%, respectively. Exercise therapy showed significant inhibition of CD in correlation between MoCA-J and diabetes treatment. BMI, insulin history, sulfonylurea history, solitary life, CK, ASO were chosen by stepwise regression, which objective variable was MoCA-J. Age and BMI were significant objective variable by age correction. Conclusions: MMSE and MoCA-J showed significant correlation with treatment background of diabetes patients. MoCA-J is especially thought to be an eminent screening method for MCI in diabetes patients. Disclosure M. Okamoto: None. J. Itoh: None. N. Sonoda: None. Y. Maeda: None. M. Fujii: None. Y. Ogawa: Research Support; Self; AstraZeneca. Advisory Panel; Self; Gilead Sciences, Inc.. Research Support; Self; Japan Agency for Medical Research and Development, Japan Society for the Promotion of Science, Mitsubishi Tanabe Pharma Corporation, Mochida Pharmaceutical Co., Ltd., Sumitomo Dainippon Pharma Co., Ltd..
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".