Estimation of Survival with Parkinson’s disease in the Canadian Longitudinal Study on Aging: An analysis using only current disease durations observed at baseline
Bibliographic record
Abstract
ABSTRACT Previous studies of survival with Parkinson’s disease (PD) have relied primarily on survival data from incident PD cohort studies with follow-up, prevalent PD cohort studies with follow-up, or a combination of these two study types. Each imposes logistical and resource constraints because of the length of follow-up required. Here, by using only the current disease durations for prevalent PD cases at study baseline we propose a strategy that does not require follow-up when estimating survival with PD. We apply our methods to data collected as part of the Canadian Longitudinal Study on Aging (CLSA). Using the reported disease durations for 110 CLSA participants classified as having prevalent Parkinson’s Disease at baseline, the estimated median survival was 5.72 years or 5.94 years, depending on how we accounted for possible uncertainty in the recalled diagnosis dates. Our results suggest a probability of at least 0.12 of surviving more than 15 years with PD. Our estimates are lower than those from other studies, which have entailed lengthy follow-up. We use only baseline data from a study that it is projected to last for twenty years. Under certain constraints our methods can be applied to other diseases and where “early” results are desired.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".