Therapeutic Efficacy and Prediction of 18F-FDG PET/CT-Assisted Botulinum Toxin Therapy in Patients With Idiopathic Cervical Dystonia
Bibliographic record
Abstract
PURPOSE: This study aimed to investigate the therapeutic efficacy of 18F-FDG PET/CT-assisted botulinum toxin (BTX) injection therapy and predictive PET findings in relation to a good response in patients with idiopathic cervical dystonia (ICD). MATERIALS AND METHODS: A total of 78 patients was enrolled from November 2007 to July 2018. The Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) score was determined at baseline and 4 weeks after BTX injection guided by electromyography and PET/CT. The number of hypermetabolic muscles, the highest SUVmax among hypermetabolic muscles, and the total SUVmax of hypermetabolic muscles were evaluated as pretreatment PET parameters. A good response was defined as a reduction rate ≥30% and a point decrease ≥15 of the TWSTRS total score. RESULTS: Half of the subjects showed a good response. Good responders had significantly higher baseline TWSTRS scores than poor responders (total score, P < 0.001; severity, P < 0.001; disability, P < 0.001; pain, P = 0.026). Good responders also had significantly higher numbers of hypermetabolic muscles and BTX-injected hypermetabolic muscles (P < 0.001, both). In multivariable analysis, the baseline TWSTRS disability subscale score and the number of BTX-injected hypermetabolic muscles were significant predictors for good response (P = 0.001 and P = 0.028). The aforementioned 3 PET parameters were positively correlated with the baseline TWSTRS scores. In addition, PET/CT well detected dystonic deep cervical muscles. CONCLUSIONS: FDG PET/CT-assisted BTX injection therapy showed good therapeutic efficacy in ICD patients. The numbers of hypermetabolic cervical muscles and BTX-injected hypermetabolic muscles may be helpful in predicting a good response.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".