Pragmatic Approach on Neuroimaging Techniques for the Differential Diagnosis of Parkinsonisms
Bibliographic record
Abstract
BACKGROUND: Rapid advances in neuroimaging technologies in the exploration of the living human brain also apply to movement disorders. However, the accurate diagnosis of Parkinson's disease (PD) and atypical parkinsonian disorders (APDs) still remains a challenge in daily practice. METHODS: We review the literature and our own experience as the Movement Disorder Society-Neuroimaging Study Group in Movement Disorders with the aim of providing a practical approach to the use of imaging technologies in the clinical setting. RESULTS: The enormous amount of articles published so far and our increasing recognition of imaging technologies contrast with a lack of imaging protocols and updated algorithms for differential diagnosis. The distinctive pathological involvement in different brain structures and the correlation with imaging findings obtained with magnetic resonance, positron emission tomography, or single-photon emission computed tomography illustrate what qualitative and quantitative measures may be useful in the clinical setting. CONCLUSION: We delineate a pragmatic approach to discuss imaging technologies, updated imaging algorithms, and their implications for differential diagnoses in PD and APDs.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".