Co-morbidities of persons dying of Parkinson's disease
Bibliographic record
Abstract
INTRODUCTION: Disease interactions can alter functional decline near the end of life (EOL). Parkinson's disease (PD) is characterized by frequent occurrences of co-morbidities but data challenges have limited studies investigating co-morbidities across a broad range of diseases. The goal of this study was to describe disease associations with PD. METHODS: We conducted an analysis of death certificate data from 1998 to 2005 in Nova Scotia. All death causes were utilized to select individuals dying of PD and compare with the general population and an age-sex-matched sample without PD. We calculated the mean number of death causes and frequency of disease co-occurrence. To account for the chance occurrence of co-morbidities and measure the strength of association, observed to expected ratios were calculated. RESULTS: PD decedents had a higher mean number of death causes (3.37) than the general population (2.77) and age-sex-matched sample (2.88). Cancer was the most common cause in the population and matched sample but fifth for those with PD. Cancer was one of nine diseases that occurred less often than what would be expected by chance while four were not correlated with PD. Dementia and pneumonia occurred with PD 2.53 ([CI] 2.21-2.85) and 1.83 (CI 1.58-2.08) times more often than expected. The strength of association for both is reduced but remains statistically significant when controlling for age and sex. DISCUSSION: Those with PD have a higher number of co-morbidities even after controlling for age and sex. Individuals dying with PD are more likely to have dementia and pneumonia, which has implications for the provision of care at EOL.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".