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Record W2392382140

Application of low frequency repetitive transcranial magnetic stimulation in treatment of spasmodic torticollis

2015· article· en· W2392382140 on OpenAlexaboutno aff
Long Yu-zho

Bibliographic record

VenueShiyong yixue zazhi · 2015
Typearticle
Languageen
FieldMedicine
TopicFacial Nerve Paralysis Treatment and Research
Canadian institutionsnot available
Fundersnot available
KeywordsSpasmodic TorticollisTranscranial magnetic stimulationBotulinum toxinTorticollisMedicinePhysical therapyRating scaleVisual analogue scalePhysical medicine and rehabilitationAnesthesiaPsychologyStimulationSurgeryInternal medicineDevelopmental psychology
DOInot available

Abstract

fetched live from OpenAlex

Objective To explore the application value of low frequency repetitive transcranial magnetic stimulation(r TMs) in spasmodic torticollis patients. Methods Seventy-eight patients with spasmodic torticollis were randomly divided into the low frequency repetitive transcranial magnetic stimulation treatment group and the botulinum toxin type A group. Both two groups were assessed by using the Toronto torticollis rating scale(TWSTRS) and the Tsui′s scale before treatment and one week, one month, three months, six months after the treatment. Results At one week,one month, three months after the r TMs treatment,the severity,disability,pain of Tsui scores were increased significantly in the r TMs treatment group(P 0.05).Compared with the botulinum toxin type A group,no significant statistical differences were found between these two groups(P 0.05).Conclusions The effect of low frequency r TMs in the treatment for spasmodic torticollis is significant. It can be used as a noninvasive physical treatment for spasmodic torticollis.

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 score0.049
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.033
GPT teacher head0.326
Teacher spread0.292 · 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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