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Record W4391471362 · doi:10.3390/toxins16020076

Botulinum Toxin Injections to the Obliquus Capitis Inferioris Muscle for Dynamic Cervical Dystonia Improves Subjective Patient Outcomes

2024· article· en· W4391471362 on OpenAlexaffabout
Robin Bessemer, Mandar Jog

Bibliographic record

VenueToxins · 2024
Typearticle
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsCervical dystoniaBotulinum toxinDystoniaMedicinePhysical therapyRetrospective cohort studyTorticollisPhysical medicine and rehabilitationMovement disordersAnesthesiaInternal medicineSurgeryPsychiatryDisease

Abstract

fetched live from OpenAlex

The obliquus capitis inferioris (OCI) muscle is a significant driver of cervical dystonia with torticaput movements and a no–no head tremor. Limited data are available on the efficacy of OCI injections on patient outcomes. Our study aims to determine whether the botulinum toxin injection into OCI improves subjective patient quality of life in those with dystonic head tremors. A retrospective chart review was performed for 25 patients receiving injections into the OCI for a dystonic head tremor at the London Movement Disorders Clinic between January 2020 and January 2022. Toronto Western Spasmodic Torticollis Scale-2 (TWSTRS-2) subscale scores for disability and pain, TWSTRS-PSYCH scores, and the global impression of severity were extracted. The average TWSTRS-2 disability subscale change was −2.8 points (p < 0.003). The average TWSTRS-2 pain subscale change was −4.6 points (p < 0.003). The average TWSTRS-PSYCH score prior to injection was 5.6. After injection, the average score was 3.7 (p < 0.004). The patient self-reported average global impression of severity before injection was 7.0; after this, it was 4.2 (p < 0.0003). The OCI injection showed significant improvement in retrospective patient self-reported outcomes; it should be considered early in the treatment plan for cervical dystonia with a no–no head tremor.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.673
Threshold uncertainty score0.700

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.279
Teacher spread0.269 · 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 designOther design
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

Citations6
Published2024
Admission routes2
Has abstractyes

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