Usefulness of<sup>18</sup>F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Management of Cervical Dystonia
Bibliographic record
Abstract
OBJECTIVE: To evaluate the usefulness of (18)F-fluorodeoxyglucose positron emission tomography/computed tomography ((18)F-FDG PET/CT) in the management of cervical dystonia (CD) with botulinum toxin type A (BoNT-A) injection. METHOD: Thirty two subjects with CD were included. A BoNT-A injection was provided either by clinically targeting method (group 1) or by (18)F-FDG PET/CT-assisted, clinically targeting method (group 2). In group 2, selection of target muscles and dosage of BoNT-A were determined according to the increased (18)F-FDG uptake, in addition to physical examination and functional anatomy. The outcomes of BoNT-A injection was compared between the two groups, in terms of the number of subjects who had reinjection before and after 6 months, the number of reinjections, the interval of reinjections, the duration to the minimal Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS), the number of adverse events, the reduction rate of TWSTRS at 1-3 months and 3-6 months after injection, and the probability of reinjection-free living. RESULTS: The number of subjects who had reinjection within 6 months was significantly lower in group 2 than in group 1 (10 in group 1 vs. 3 in group 2). The reduction rate of TWSTRS after 3-6 months (37.8±15.7% of group 1 vs. 63.3±28.0% of group 2) and the probability of reinjection-free living were significantly higher in group 2 than in group 1. CONCLUSION: These findings suggest that (18)F-FDG PET/CT study could be useful in management of CD in terms of the identification of dystonic muscles if there is an increase in the (18)F-FDG uptake in the cervical muscle of the images.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".