Microsurgical denervation in treatment of focal cervical muscular dystonia: 72 cases analysis
Bibliographic record
Abstract
Objective: To analyze the results of microsurgical denervation in spasmodic torticollis patients.Materials and methods: 72 patients with spasmodic torticollis were enrolled into the study. All enrolled patients underwent 154 microsurgical denervations of dystonic muscles, including 58 selective denervations of sternocleidomastoid muscle, 66 selective posterior ramisectomy of C1-C6 rootlets (Bertrand’s procedure), 30 denervations and myotomias of dystonic muscles of omo-trapezoid triangle (DMOTT). The outcome evaluation was conducted via neurological examination and Toronto Western Spasmodic Torticollis Rating Scale questionnaire.Results: Initial number of severe torticollis patients was 25 (34.72%), moderate severity – 40 (55.55%), mild severity – 7 (9.72%), during the analysis of long-term outcomes – 0 (0%), 38 (57.57%) and 28 (42.42%) respectively. Initial severe disability was in 44 patients (61.11%), moderate – in 25 (34.72%), mild – in 3 (4.16% ) patients, during the analysis of long-term outcomes – in 3 (4.54%), 34 (51.51%) and 29 (43.93%) patients respectively.Conclusions: Average index of good outcomes, which included morbid severity and disability severity, was 64.39%. In 35.61% of patients the outcomes of treatment were less satisfactory due to presence of residual pathological movements, neuralgia of occipital nerve, disesthesia in C2 dermatome, transient weakness of trapezoid muscle.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".