Dopamine transporter imaging is associated with long‐term outcomes in Parkinson's disease
Bibliographic record
Abstract
Dopamine (DA) transporter (DAT) imaging has been studied as a diagnostic tool for degenerative parkinsonism. Our aim was to measure the prognostic value of imaging for motor and nonmotor outcomes in Parkinson's disease (PD). We prospectively evaluated a Parkinson's cohort after enrollment in a de novo clinical trial with a battery of motor (UPDRS), cognitive (Montreal Cognitive Assessment), and behavioral measures. DAT imaging with [(123)I][β]-CIT and single-photon emission computerized tomography (SPECT) was performed at baseline and after 22 months. In total, 491 (91%) of the 537 subjects had evidence of DA deficiency on their baseline scan, consistent with PD, and were included in the analyses. The cohort was followed for 5.5 (0.8) years, with a mean duration of diagnosis of 6.3 (1.2). Lower striatal binding at baseline was independently associated with higher risk for clinical milestones and measures of disease severity, including motor-related disability, falling and postural instability, cognitive impairment, psychosis, and clinically important depressive symptoms. Subjects in the bottom quartile for striatal binding, compared to the top quartile, had an odds ratio (95% confidence interval) of 3.3 (1.7, 6.7) for cognitive impairment and 12.9 (2.6, 62.4) for psychosis. Change from baseline in imaging after 22 months was also independently associated with motor, cognitive, and behavioral outcomes. DAT imaging with [(123)I][β]-CIT and SPECT, shortly after the diagnosis of PD, was independently associated with clinically important long-term motor and nonmotor outcomes. These results should be treated as hypothesis generating and require confirmation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".