Analysis of the Comprehensive Treatment for Patients with Severe Dementia in Alzheimer's Disease in Community
Bibliographic record
Abstract
Objective To analyze the treatment effect of patients with severe dementia in Alzheimer's disease in community.Methods 13 cases of patients with severe dementia in Alzheime's disease in community were selected and given the comprehensive treatment. The revised Hasegawa Dementia Scale(HDS-R), Activity of Daily Living Scale(ADL) and Concise Montreal cognitive assessment scale(MoCA) were used to evaluate and the efficacy of the patients before treatment and at 3, 6 months after treatment was compared, respectively. Results After 6 months of treatment, the Activity of Daily Living Scale was 25.37±4.67, the difference was statistically significant compared with that before treatment, P0.05; in MoCA scale, only the score of the three aspects of name, language, directional force was higher than that of before treatment, respectively, and which was 2.20 ±0.71, 1.27±0.41,3.89 ±0.91, respectively, the differences were statistically significant, P 0.05; the difference in scores of other four aspects and HDS-R after treatment were not significant as compared with those before treatment, P0.05. Conclusion The clinical treatment of dementia in Alzheimer's disease is very difficult to obtain a satisfactory effect, and more attention should be paid to early prevention and the intervention should be carried on at the stage of mild cognitive impairment.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".