Exploring Genetic Influences On Variation In The Phenotypic Expression Of Parkinson’s Disease. (P2.029)
Bibliographic record
Abstract
Objective: To investigate the influence of genotype on the phenotypic expression of Parkinson’s disease (PD). Background: A United Kingdom based multi-centre longitudinal study, the Parkinson’s Repository of Biosamples and Networked Datasets (PRoBaND), also known as Tracking Parkinson’s, is on-going. This is an interim report from the study. Methods: Testing was undertaken for common PD gene mutations. Validated clinical scales and questionnaires were used. Results: 191 early onset (diagnosed < 50 years old), and 548 late onset cases have been tested, out of 1860 recruited. 95% are Caucasian; two-thirds are male. In early onset cases, 19 (10%) tested positive (5 LRRK2, 11 GBA, 3 PARKIN), aged 51.6 (SD 6.2), disease duration 12.7 years (SD 8.8); and 173 (90%) tested negative, aged 52.6 (SD 8.3), disease duration 10.0 years (SD 6.6). The total unified PD rating scale (UPDRS) score in gene test positives was 70.4 (SD 26.8) vs 67.1 (SD 25.7) in gene test negatives; Montreal cognitive assessment (MOCA) score was 21.3 (SD 4.1) vs 22.3 (SD 3.5), and quality of life (QOL) score was 36.1 (SD 17.3) vs 35.9 (SD 17.9). In late onset cases, 20 tested positive (3.7%) (6 LRRK2, 14 GBA), aged 59.5 (SD 13.4), disease duration 1.7 years (SD 1.0); and 528 tested negative (96.3%), aged 65.1(SD 13.1), disease duration 1.4 years (SD 1.0). The total UPDRS score in gene test positives was 48.5 (SD 21.6) vs 40.9 (SD 19.4) in gene test negatives; MOCA score was 21.5 (SD 5.8) vs 19.9 (SD 7.1), and QOL score was 19.3 (SD 14.2) vs 21.0 (SD 15.1). Conclusion: Early onset PD is more likely to test positive for known gene mutations, as expected. Further analysis extending the scope of genetic testing, and linked to progression rates, is planned. Supported by: Parkinson's UK
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".