Association between positive history of essential tremor and disease progression in patients with Parkinson's disease
Bibliographic record
Abstract
This study aimed to explore the effect of pre-existing essential tremor (ET) history on the disease progression of Parkinson's disease (PD). We recruited and followed-up a group of PD patients from March 2009 to July 2020. The ET history of each patient was obtained by retrospective interviews or past medical records. Cox proportional hazards models with inverse probability of treatment weighting (IPTW) were used to estimate the hazard ratio (HR) with 95% confidence intervals (CIs). Of 785 patients who completed the followed-up visits, 61 patients (7.8%) reported a history of pre-existing ET. Cox regression models after IPTW indicated that the positive ET history in patients with PD was protective against time to United PD Rating Scale III 14-point increase (HR = 0.301, 95% CI = 0.134-0.678, P = 0.004), time to akinesia and rigidity 8-point increase (HR = 0.417, 95% CI = 0.218-0.796, P = 0.008), time to conversion to Hoehn and Yahr stage 3 (HR = 0.356, 95% CI = 0.131-0.969, P = 0.043), time to develop dyskinesia (HR = 0.160, 95% CI = 0.037-0.698, P = 0.015), and time to Montreal Cognitive Assessment 3-point decrease (HR = 0.389, 95% CI = 0.160-0.946, P = 0.037), but had no relationship with time to tremor 4-point increase (HR = 1.638, 95% CI = 0.822-3.266, P = 0.161) and time to death (HR = 0.713, 95% CI = 0.219-2.319, P = 0.574). Our study indicated that ET history in patients with PD is associated with a benign prognosis with slower motor and non-motor progression.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".