Mortality of Alzheimer’s Disease Patients: A 10-Year Follow-up Pilot Study in Shanghai
Bibliographic record
Abstract
BACKGROUND: Identifying risk factors and mortality of individuals with Alzheimer's disease (AD) could have important implications for the clinical management of AD. OBJECTIVE: This pilot study aimed to examine the overall mortality of AD patients over a 10-year surveillance period in Shanghai, China. This study is an extension of our previous investigation on mortality of neurodegenerative diseases. METHODS: One hundred and thirty-two AD patients recruited from the memory clinics of two hospitals in Shanghai in 2007 were followed up until December 31, 2017 or death, representing a follow-up period of up to 10 years. Overall standardized mortality ratios (SMRs) were calculated, and predictors for survival at recruitment were estimated. RESULTS: Sixty-seven patients had died by December 31, 2017, and the SMR at 10 years of follow-up was 1.225 (95% confidence interval 0.944-1.563). Employing Cox's proportional hazard modeling, lower Mini-Mental State Examination score, and comorbid diabetes predicted poor survival in this cohort. CONCLUSION: This pilot study suggests a similar survival trend of patients with AD compared to the general population in Shanghai urban region. Poor cognitive status and comorbid diabetes had a negative impact on the survival of AD 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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".