Value of Clinical Signs in Identifying Patients with Scans without Evidence of Dopaminergic Deficit (SWEDD)
Bibliographic record
Abstract
BACKGROUND: In clinical trials that recruited patients with early Parkinson's disease (PD), 4-15% of the participants with a clinical diagnosis of PD had normal dopamine transporter single photon emission computed tomography (DAT SPECT) scans, also called "scans without evidence of dopaminergic deficit" (SWEDD). OBJECTIVE: To investigate in patients with a clinical diagnosis of PD, if specific clinical features are useful to distinguish patients with nigrostriatal degeneration from those that have no nigrostriatal degeneration. METHODS: We performed a diagnostic test accuracy study. Patients that participated in the Levodopa in Early Parkinson's disease trial, a clinical trial in patients with early PD, were asked to participate if they had not undergone DAT SPECT imaging earlier. The index tests were specific clinical features that were videotaped. A panel of six neurologists in training (NT), six general neurologists (GN), and six movement disorders experts (MDE) received a batch of ten videos consisting of all SWEDD subjects and a random sample of patients with abnormal DAT SPECT scans. The raters analyzed the videos for presence of specific signs and if they suspected the patient to have SWEDD. The reference test was visually assessed DAT SPECT imaging. RESULTS: Of a total of 87 participants, three subjects were SWEDDs (3.4%). The overall intraclass correlation coefficient (ICC) of the Parkinsonian signs was poor to moderate with ICCs ranging from 0.14 to 0.67. NT correctly identified 50.0% of the SWEDD subjects, GN 33.3%, and MDE 66.7%. CONCLUSION: Our study suggests that the selected videotaped clinical features cannot reliably distinguish patients with a clinical diagnosis of PD and an abnormal DAT SPECT from patients with clinical PD and a SWEDD.
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".