Hereditary parkinsonism: Parkinson disease look‐alikes—An algorithm for clinicians to “<i>PARK</i>” genes and beyond
Bibliographic record
Abstract
In the past decade, a number of genetic causes of parkinsonism have been identified. As a consequence, clinicians have to consider an increasing range of differential diagnoses when confronted with a patient with parkinsonism with a positive family history. While well-established monogenic forms with PARK acronyms have been reviewed extensively, less emphasis has been placed on other inherited conditions that may also present with signs of parkinsonism or even mimic idiopathic Parkinson's disease clinically. In this review, we focus on three different scenarios in patients with an overall early age of onset of parkinsonism: (i) atypical features in patients with mutations in one of the "PARK" genes; (ii) classical parkinsonism due to mutations in "other than-PARK" genes or yet other genes where parkinsonism may be a well-recognized, concomitant, or even an isolated feature; (iii) atypical parkinsonism in other genetic disorders which are, however, typically characterized by features other than parkinsonism. Atypical features in patients from Group I include, for example, a slower disease course (PARK2, PARK6, PARK7) or dementia (PARK1/4, PARK14). Conditions in Group II have been designated by a DYT or SCA acronym (for example, DYT5 or SCA3) and also include patients with heterozygous GBA mutations, mitochondrial gene mutations. Group III comprises mutations in the FMR1, MAPT, GRN, ATP7B, PANK2, FBXO7, CHAC, FTL1, Huntingtin, JPH3 genes, and a number of even rarer, miscellaneous conditions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".