Mortality from Parkinson’s disease in China: Findings from a five-year follow up study in Shanghai
Bibliographic record
Abstract
INTRODUCTION: The mortality of Parkinson's disease (PD) and its associated risk factors among clinically definite PD patients in China has been rarely investigated. Our study aimed to identify the mortality rates and predictors of death in PD patients in China. METHODS: 157 consecutive, clinically definite PD patients from the urban area of Shanghai were recruited from a central hospital based movement disorder clinic in 2006. All patients were regularly followed up at the clinic until December 31, 2011, or death. Mortality and associations with baseline demographics, health and medical factors were then determined within the cohort. RESULTS: After 5 years, 11(7%) patients had died. The standardised mortality ratio was 0.62 (95% CI 0.32 to 1.07, P=0.104). The main causes of death were pneumonia (54.5%, 6/11) and digestive disorders (18.2%, 2/11), respectively. Age at onset, independent living, the mini mental state examination score, the Parkinson's disease sleep scale score and the Epworth sleepiness scale score at baseline were statistically significantly different between the survival group and the deceased group (P<0.05). Across all participants, risk factors for death included low mini mental state examination score, and high Epworth sleepiness scale score according to a binary variable logistic regression analysis. CONCLUSIONS: This study confirms the similar survival of patients with PD to the control population up to a follow-up of 5 years. Interventions tailored to potential risk factors associated with death may offer further benefits.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".