Analysis of clinical diagnosis and treatment of Alzheimer disease in Tianjin,China
Bibliographic record
Abstract
【Objective】To review the clinical diagnosis and treatment status of Alzheimer disease(AD), and evaluate the efficacy. 【Methods】A study about AD was performed in first-grade hospitals in Tianjin China between March 2011 and May 2013. AD patients treated with oxiracetam, donepezil, memantine, combination(donepezil and memantine) were regularly arranged with follow-up evaluation through Mini-Mental State Examination(MMSE), Activities of daily living(ADL) and Montreal Cognitive Assessment Beijing Version(MOCA-BJ).【Results】A total of 450 AD patients(52.7% female, mean age 70.6 years) were included. According to baseline MMSE, AD patients were divided in three groups: mild group(156 cases, 34.67%), moderate group(209 cases, 46.44%) and severe group(85 cases, 18.89%), which were significantly different in gender, symptom duration and educational level(P 0.01). Different groups were oxiracetam(11 cases, 2.44%), donepezil(198 cases, 44%), memantine(71 cases, 15.78%), combination(donepezil and memantine, 58 cases, 12.89%), and nontreatment(112 cases,24.89%). There were significantly difference in treatment groups in the duration and baseline assessment(MMSE, MOCA-BJ, ADL)(P = 0.000). No differences were observed between memantine and combination(P 0.05), and difference was observed between donepezil and memantine /combination(P 0.01). In the 2-year follow-up evaluation, nontreatment group showed faster cognitive decline than any treatment group.【Conclusion】During first diagnosis most AD patients were on the moderate or severe stage. The selection of anti-dementia medicines was directly related to the symptom duration and baseline assessment(MMSE, MOCA-BJ, ADL). Early positive diagnosis and treatment of AD can effectively delay their cognitive and functional deterioration and improve their cognitive function.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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".