Head injury, α‐synuclein genetic variability and <scp>P</scp>arkinson's disease
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Head injury has been linked to Parkinson's disease (PD) in some but not all studies. Differences in the genetic and environmental susceptibility to PD between populations might be one explanation. The joint effects of head injuries and SNCA genetic variants were investigated. METHODS: From 2001 to 2012, 561 incident idiopathic PD cases and 721 population controls from central California were enrolled. Subjects reported on head injuries throughout their lifetime and were assessed for genetic variability in the SNCA 5' region (D4S3481; Rep1) and 3' untranslated region (rs356165). In unconditional logistic regression models adjusted for confounders, interactions between head injuries and genetic risk variants were investigated. RESULTS: Parkinson's disease risk in individuals with head injury who are carriers of at least one 263 bp allele in D4S3481 or rs356165 variants was 3-4.5-fold higher compared with non-carriers without head injuries. However, tests for interaction between head injury and SNCA D4S3481or rs356165 were not statistically significant. CONCLUSIONS: Our study finds some evidence that head injury and D4S3481 or rs356165 variants jointly increase the risk of PD but little evidence of interaction.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".