Analysis of the Effect of Dopamine Transporter Scan on the Diagnosis and Management in a Tertiary Neurology Center
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The dopamine transporter scan or DaT scan is abnormal in presynaptic parkinsonism but normal in nondegenerative or postsynaptic parkinsonism. In this study, we tried to ascertain the impact of DaT scan on the diagnosis and clinical management and if the semiquantitative analysis of the DaT scans has any correlation with the clinical symptoms. METHODS: The electronic and nonelectronic records of patients of Plymouth Hospital NHS Trust, United Kingdom, from 2011 to 2015 were studied to find the indication, outcome, and the impact of the scan on the management of patients. The DaT scan results were assessed visually and semiquantitatively by the Department of Nuclear Medicine. The available data were statistically analyzed with the help of Microsoft XL2010 and GraphPad software. RESULTS: A total of 258 people had DaT scan. The scan results suggested an alternate diagnosis in 50.5% of clinically diagnosed patients with Parkinson disease. Similarly, DaT scan changed the diagnosis of 40% of patients with clinical diagnosis of vascular parkinsonism, 25% of clinically diagnosed drug-induced parkinsonism, and 54% of patients with possible Lewy body dementia. Visual assessment of the DaT scan revealed that more than 60% had grade 2 abnormalities. The distribution volume ratio, a semiquantitative tool for tracer uptake, was significantly less in the patients with akinetic-rigid subtype of Parkinson disease in comparison to a tremor predominant subtype. CONCLUSIONS: Dopamine transporter scan had a significant impact in diagnosis and management.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".