Bibliographic record
Abstract
Parkinson's disease (PD) is a neurodegenerative disease with limited pharmacologic therapies. Recent animal studies and one large retrospective study have found NSAIDs to be protective against the development of PD. We decided to test this hypothesis by conducting a nested case-control study using the Saskatchewan drug plan database. Entry to the cohort was defined as the first prescription of an antihypertensive agent between 1980 and 1987 and followed until 1999. Cases were defined as those having received three prescriptions for a dopamine agonist within a year. For each case, ten controls were selected matched to the case by age, calendar time and index date. Conditional logistic regression was used to estimate rate ratios adjusting for gender, previous use of arthritis medication and previous antipsychotic use. Current users of NSAIDs had a slightly higher risk of developing PD (RR=1.49 [95% CI, 1.11-2.01]). This effect was not seen with past users (RR=1.18 [95% CI, 0.89-1.59]). Based on the results of our study current users of NSAIDs may be at a slightly higher risk of developing PD. More studies are needed to confirm this finding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".