Leucine-Rich Repeat Kinase 2 Gene-Associated Disease: Redefining Genotype-Phenotype Correlation
Bibliographic record
Abstract
BACKGROUND: Leucine-rich repeat kinase 2 (LRRK2) has emerged as the most prevalent genetic cause of Parkinson's disease (PD) among Caucasians. Patients carrying an LRRK2 mutation display significant variability of clinical and pathologic phenotypes across and within affected families. METHODS: Herein, we review available clinical and pathologic data on patients with an LRRK2 mutation who have come to autopsy. RESULTS: Thirty-eight patients have been reported who presented clinically with PD; parkinsonism with resistance to levodopa, supranuclear gaze palsy, or autonomic dysfunction; or tremor and dementia. Pathology showed typical PD-type Lewy body disease (LBD) in most patients, whereas in others there was 'pure' nigral degeneration (one with TDP-43-positive inclusions), diffuse LBD, or tau-, alpha-synuclein- or ubiquitin-positive pathology reminiscent of progressive supranuclear gaze palsy, multisystem atrophy, and frontotemporal dementia with ubiquitin-positive inclusions. CONCLUSIONS: Such clinical and pathologic variability suggests Lrrk2 acts upstream from other proteins implicated in neurodegeneration. Specific mutations may be associated with alternative progressive supranuclear gaze palsy-like or 'pure' nigral degeneration phenotypes. A different effect on Lrrk2 kinase activity may play a role in such heterogeneity.
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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 | 0.000 |
| 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".