The genetics of <scp>P</scp>arkinson's disease: Progress and therapeutic implications
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
The past 15 years has witnessed tremendous progress in our understanding of the genetic basis for Parkinson's disease (PD). Notably, whereas most mutations, such as those in SNCA, PINK1, PARK2, PARK7, PLA2G6, FBXO7, and ATP13A2, are a rare cause of disease, one particular mutation in LRRK2 has been found to be common in certain populations. There has been considerable progress in finding risk loci. To date, approximately 16 such loci exist; notably, some of these overlap with the genes known to contain disease-causing mutations. The identification of risk alleles has relied mostly on the application of revolutionary technologies; likewise, second-generation sequencing methods have facilitated the identification of new mutations in PD. These methods will continue to provide novel insights into PD. The utility of genetics in therapeutics relies primarily on leveraging findings to understand the pathogenesis of PD. Much of the investigation into the biology underlying PD has used these findings to define a pathway, or pathways, to pathogenesis by trying to fit disparate genetic defects onto the same network. This work has had some success, particularly in the context of monogenic disease, and is beginning to provide clues about potential therapeutic targets. Approaches toward therapies are also being provided more directly by genetics, notably by the reduction and clearance of alpha-synuclein and inhibition of Lrrk2 kinase activity. We believe this has been an exciting, productive time for PD genetics and, furthermore, that genetics will continue to drive the etiologic understanding and etiology-based therapeutic approaches in this disease.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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