The efficacy of single-photon emission computed tomography in identifying dystonic muscles in cervical dystonia
Bibliographic record
Abstract
INTRODUCTION: The key point for botulinum toxin type A injection in treating cervical dystonia is to accurately identify dystonic muscles. This study aimed to evaluate the efficacy of technetium-sestamibi single-photon emission computed tomography in identifying target muscles in cervical dystonia. METHODS: In the study group (n = 18), target muscles were selected according to clinical evaluation combined with technetium-sestamibi single-photon emission computed tomography, while in the control group (n = 18), target muscles were selected by clinical evaluation alone. All patients were followed-up at 2 weeks, 1, 3 and 6 months after botulinum toxin type A injection. The primary outcomes were the reduction rates in Toronto Western Spasmodic Torticollis Rating Scale and Tsui score at 1 month. RESULTS: Although the reduction rates in Toronto Western Spasmodic Torticollis Rating Scale and Tsui scores were not different between the two groups at 2 weeks and 1 month, the reduction rates in both scores were significantly higher in the study group at 3 and 6 months. The number of patients receiving re-injection within 6 months was significantly lower in the study group. Also, the re-injection interval was significantly longer in the study group. In the study group, more deep cervical muscles were injected, which concerns especially semispinalis capitis, longissimus capitis, and obliques capitis inferior muscles. CONCLUSION: technetium-sestamibi single-photon emission computed tomography is a useful method for screening target muscles in cervical dystonia. It helps clinicians draw a 'blueprint' for the distribution of dystonic muscles before botulinum toxin type A injection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".