Parkinson's Disease, Multiple Sclerosis and Changes of Residence in Alberta
Bibliographic record
Abstract
BACKGROUND: Our objective is to examine how persons diagnosed with Multiple Sclerosis (MS) and Parkinson's disease (PD) change residence following disease onset. We hypothesize that persons choose to change residence (locally or regionally) in different ways depending on whether or not they have been diagnosed with MS/PD. We also estimate the effects of residence change on measures of disease prevalence made at several different levels of geography. METHODS: Using fee-for service and hospitalization data, we identify cases of MS and PD between 1994 and 2004. Both of these case groups are matched to controls based on age, sex, socioeconomic status and municipality of residence. We tabulate and compare the changes of residence among persons in the case and control groups. We also use these data to estimate the effects that changes in residence have on disease prevalence at three different levels of geography. RESULTS: Both MS and PD patients were more likely to change residence following disease onset compared to groups of matched controls (p<=0.001). Most changes of residence occur within the same municipality. The total magnitude of these changes is small, however, and is unlikely to affect estimates of disease prevalence; over our study period, the largest change in geographical prevalence estimates due to individual changes in residence was about 1%. CONCLUSIONS: Persons diagnosed with MS and PD both have mobility characteristics that differ from those of their respective control groups, and in general, are more likely to move to or between Edmonton and Calgary, and less likely to move out of province. However, the balance of mobility characteristics of persons with PD and MS appear unlikely to greatly affect the patterns observed on maps of disease prevalence.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".