Exposure to Phosphoglycerate Kinase 1 Activators and Incidence of Parkinson's Disease
Bibliographic record
Abstract
BACKGROUND: Preclinical and epidemiological data suggest that phosphoglycerate kinase 1 activators could have neuroprotective properties and prevent PD. OBJECTIVES: The objective of this study was to compare the association between increased use of phosphoglycerate kinase 1 activators and increased use of tamsulosin with PD incidence. METHODS: Our retrospective cohort study included men older than age 66 years newly exposed to phosphoglycerate kinase 1 activators or tamsulosin and compared their PD incidence, using health care administrative data of Ontario, Canada. RESULTS: Among 265,745 men, each additional year of cumulative use of phosphoglycerate kinase 1 activators or tamsulosin was associated with 6% and 8% reduction, respectively, in the hazard of PD incidence. These hazards were not significantly different (P = 0.2094). A secondary analysis with the observation window starting after 6 months and 1 and 2 years showed similar results. CONCLUSIONS: Increasing exposure to phosphoglycerate kinase 1 activators and tamsulosin were both associated with small reductions in PD incidence. These results support further investigation of phosphoglycerate kinase 1 activators and tamsulosin for possible PD disease-modifying properties. © 2021 International Parkinson and Movement Disorder Society.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".