Gout and the risk of parkinson's disease: A cohort study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Several studies have suggested that higher serum uric acid levels lead to a lower risk of Parkinson's disease (PD) because uric acid exerts antioxidant effects on neurons. Our objective was to examine the relationship between gout and the risk of PD in persons age > or = 65 years. METHODS: We conducted a population-based cohort study using the British Columbia Linked Health Database and PharmaCare data (i.e., prescription drug data for those age > or = 65 years). We compared incidence rates of PD between 11,258 gout patients and 56,199 controls matched on age, sex, date of gout diagnosis, and length of medical record. Cox proportional hazards models were used to estimate the relative risk (RR) of PD, adjusting for age, sex, prior comorbid conditions, and use of diuretics and nonsteroidal antiinflammatory drugs. RESULTS: Over an 8-year median followup, we identified 1,182 new cases of PD. Compared with individuals without gout, the multivariate RR of PD among those with gout was 0.70 (95% confidence interval [95% CI] 0.59-0.83). In subgroup analyses, the inverse association was similarly present in both sexes and was evident among those who did not use diuretics (RR 0.66, 95% CI 0.54-0.81), but not among diuretic users (RR 0.80, 95% CI 0.58-1.10, P for interaction 0.35). CONCLUSION: Our population-based data provide evidence for a protective effect of gout on the risk of PD and support the purported protective role of uric acid.
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.
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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it