Early-onset vs. Late-onset Parkinson’s disease: A Clinical-pathological Study
Bibliographic record
Abstract
BACKGROUND: Several studies have compared early-onset Parkinson disease (EOPD) and late-onset Parkinson disease (LOPD) but most are not based on autopsy confirmed cases. METHODS: We compared clinical and pharmacological profiles, time to reach irreversible Hoehn and Yahr (H&Y) Stage 3 and levodopa motor complications in autopsy confirmed EOPD and LOPD cases. RESULTS: At first clinic visit EOPD cases were younger but had longer disease duration and they died at a younger age (all p<0.0001). Anti-Parkinsonian drug use, including levodopa, was significantly delayed in EOPD. Lifetime use of amantadine (p<0.05) and dopamine agonists (p<0.01) were higher in EOPD. While lifetime use of levodopa was similar in the two groups, levodopa was used for a significantly longer period by EOPD (p< 0.0001). EOPD had a higher cumulative incidence of dyskinesias (p<0.01), wearing-off (p<0.01), and on-off (p<0.01). However, the time to dyskinesia onset was similar in the two groups. The threshold to wearing-off was much longer in EOPD (p<0.01). H&Y stage profile at first visit was similar in the two groups. The duration from disease onset to reach irreversible H&Y stage 3 was significantly longer in EOPD. CONCLUSIONS: Our observations indicate that progression of PD is slower in EOPD and suggest that the pre-clinical interval in this group is longer. These findings can be used for case selection for drug trials and studies of the pathogenesis of PD.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".