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Record W2235549599 · doi:10.25305/53142

Microsurgical denervation in treatment of focal cervical muscular dystonia: 72 cases analysis

2015· article· en· W2235549599 on OpenAlexaboutno aff
Vitaliy Tsymbaliuk, Ihor B. Tretyak, Mark Yu. Freidman, Aleksandr Gatskiy

Bibliographic record

VenueUkrainian Neurosurgical Journal · 2015
Typearticle
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsSpasmodic TorticollisMedicineDenervationSurgeryTorticollisCervical dystoniaDystoniaAnesthesiaBotulinum toxinAnatomy

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.840

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.064
GPT teacher head0.314
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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