Parkinson's Disease in Patients with Essential Tremor: A Prospective Clinical and Functional Neuroimaging Assessment
Bibliographic record
Abstract
Introduction: Patients with essential tremor (ET) have 3.5 times greater risk of developing Parkinson's disease (PD) throughout their lives, also known as PD with antecedent ET (ET-PD). Single photon emission computed tomography with radiotracer imaging of dopamine transporters (TRODAT-SPECT) can help differentiate these two diseases. Method: Relate the results of TRODAT-SPECT imaging in patients with ET to potential progress to ET-PD. Thirty-six patients with ET were evaluated by neurological examination, the Archimedes spiral, and the MDS-UPDRS III scale on two occasions, after a mean interval of three years. SPECT was performed on all patients after the first visit. Results: Overall, six patients (16.6%) progressed clinically to ET-PD. Patients with ET-PD were older, and the age of tremor onset was later. The ET-PD group scored higher on the MDS-UPDRS III scale, especially for the presence of bradykinesia. SPECT imaging was altered in 83.3% of the ET-PD patients compared to 33% of the ET patients (p=0.034). Changes on the SPECT with asymmetrical hypouptake suggested progress to ET-PD (p=0.025). Conclusion: Advanced age at the onset of tremor, the presence of bradykinesia, and asymmetrical alterations in SPECT may be related to progression to PD in patients with ET. Changes in neuroimaging suggest that SPECT-TRODAT can be used to predict progression to PD in selected patients.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".