Establishing diagnostic criteria for Perry syndrome
Bibliographic record
Abstract
Objective To establish international diagnostic criteria for Perry syndrome, a disorder characterised by clinical signs of parkinsonism, depression/apathy, weight loss, respiratory symptoms, mutations in the DCTN1 gene and TAR DNA-binding protein 43 (TDP-43) pathology. Methods Data from the published literature and newly identified patients were gathered and analysed during and after the International Symposium on Perry syndrome in Tokyo to identify diagnostic criteria for Perry syndrome. Results Eighty-seven patients with Perry syndrome carrying DCTN1 mutations from 20 families were included in this study, and common signs of the disorder were identified, including parkinsonism (95.2% of patients), depression/apathy (71.4%), respiratory symptoms (66.7%) and weight loss (49.2%). Conclusions Based on our findings, we propose the following definitive diagnostic criteria for Perry syndrome: the presence of four cardinal signs of Perry syndrome, accompanied by a mutation in DCTN1; or a family history of the disease, parkinsonism and a mutation in DCTN1; or the presence of four cardinal signs and pathological findings that include nigral neuronal loss and TDP-43 pathology. As patients with Perry syndrome present with uniform clinical, genetic and pathological features, we further propose the disorder be termed ‘Perry disease.’
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.003 | 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".