A.01 Parkinson’s disease prognosis by early motor subtypes
Bibliographic record
Abstract
Background: Studies of autopsy-confirmed cases suggest that Parkinson’s disease (PD) prognosis can be predicted using motor symptom severity at first visit. We evaluated the association between motor symptom subtype at first visit and severity at eight years disease duration among clinically-diagnosed cases at the Saskatchewan Movement Disorder Program. Methods: Retrospective data review identified 374 patients with first visit within three years of symptom onset, a clinical diagnosis of idiopathic PD, and a follow-up visit eight years after symptom onset. Subtypes were grouped as tremor-dominant (TD) if tremor was greater than rigidity and bradykinesia, akinetic-rigid (AR) if rigidity or bradykinesia was greater than tremor, and mixed (MX) if patient was neither TD nor AR based on assessment of all four limbs. Primary outcome was disease severity as measured by Hoehn & Yahr score at eight years after symptom onset. Results: The most common subtype was AR (n=164) followed by MX (n=156). TD was least common (n=54). There was no significant difference between subtypes in H&Y scores at eight years disease duration. Conclusions: These findings suggest that early PD prognosis cannot be predicted based on motor symptoms in all four limbs at first visit. Earlier studies had longer follow-up and future studies will examine progression at longer periods of disease duration.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".