Diagnostic Utility of [11C]DTBZ Positron Emission Tomography In Clinically Uncertain Parkinsonism: Experience of a Single Tertiary Center
Bibliographic record
Abstract
BACKGROUND: The use of single-photon emission computed tomography and positron emission tomography (PET) has proven to be helpful in differentiating Parkinson's disease (PD) from other movement disorders with a sensitivity of up to 95%. OBJECTIVE: The objective of this study was to determine the accuracy of [11C]DTBZ PET imaging in patients with clinically uncertain parkinsonism from a tertiary referral center in Mexico City. MATERIALS AND METHODS: Patients who underwent [11C]DTBZ PET brain scan due to clinically uncertain parkinsonism where divided into two groups: PD or non-PD. A scan was considered positive when visual assessment revealed a decrease in [11C]DTBZ uptake typical for PD; a scan was considered negative when visual assessment showed no decrease in [11C]DTBZ uptake or showed a decrease in tracer uptake in a non-PD pattern. Sensitivity, specificity, and positive and negative predictive values were calculated using a 2 × 2 table, with a 95% confidence interval. RESULTS: A total of 39 patients were included in the study. 14 PET studies were deemed positive and 25 PET studies were deemed negative; 12 true positives and 23 true negatives were found. This yielded a sensitivity of 92.9% (95% CI, 66.1-99.8), specificity of 92% (95% CI, 74-99), PPV of 86.7% (95% CI, 63.1-96.1), and NPV of 95.8% (95% CI, 79.1-98.4). CONCLUSIONS: The [11C]DTBZ PET has an excellent accuracy for differentiating idiopathic PD from other disorders.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".