Efficacy of single-photon emission computed tomography aided botulinum toxin injection in cervical dystonia: A double-blind, randomized study
Bibliographic record
Abstract
BACKGROUND: Although single-photon emission computed tomography (SPECT/CT) could help to predetermine dystonic muscles in patients with cervical dystonia (CD), its efficacy in aiding botulinum toxin injection is undetermined. This randomized, double-blinded study aimed to assess the efficacy of SPECT/CT aided botulinum toxin injection in CD. METHODS: Patients were randomized into study group (candidate muscles selected by SPECT/CT and clinical evaluation) or control group (clinical evaluation). Follow-ups were done at two weeks (T1), one (T2), three (T3) and six months (T4). The primary outcomes included symptom improvement assessed using Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) and Tsui score at T2. RESULTS: A total of 122 patients were enrolled and 108 patients accomplished the study. For primary outcomes, the study group had significantly better symptom improvement at T2 (TWSTRS: β, -4.86 [95%CI -9.40 to -0.32; P = 0.036]; Tsui: β, -1.65 [95%CI -2.77 to -0.54; P = 0.004]). For secondary outcomes, the study group also showed better outcomes at T1 (TWSTRS: β, -6.33 [95%CI -10.17 to -2.49; P = 0.001]; Tsui: β, -1.42 [95%CI -2.48 to -0.37; P = 0.008]) and T3 (TWSTRS: β, -6.05 [95%CI -11.09 to -1.01; P = 0.019]; Tsui: β, -1.24 [95%CI -2.40 to -0.08; P = 0.037]). The interval of re-injection was significantly longer in the study group than the control group (159.1 ± 28.6 versus 141.8 ± 51.0 days, P = 0.032). CONCLUSIONS: SPECT/CT could improve the efficacy of botulinum toxin in CD. It could become a useful tool to aid botulinum toxin injection.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".