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Record W4376275210 · doi:10.3390/toxins15050333

Pain Reduction in Cervical Dystonia Following Treatment with IncobotulinumtoxinA: A Pooled Analysis

2023· article· en· W4376275210 on OpenAlexaboutno aff
Alberto Albanese, Jörg Wissel, Wolfgang H. Jost, Anna Castagna, Michael Althaus, Georg Comes, Astrid Scheschonka, Matteo Vacchelli, Hyder A. Jinnah

Bibliographic record

VenueToxins · 2023
Typearticle
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCervical dystoniaConcomitantVisual analogue scaleAnesthesiaPhysical therapyBotulinum toxinInternal medicine

Abstract

fetched live from OpenAlex

This analysis pooled pain severity data from four phase 3 and 4 studies of incobotulinumtoxinA (incoBoNT-A) for the treatment of cervical dystonia (CD) in adults. CD-related pain severity was assessed at baseline, each injection visit, and 4 weeks after each injection of incoBoNT-A using the Toronto Western Spasmodic Torticollis Rating Scale pain severity subscale or a pain visual analog scale. Both were analyzed using a score range of 0–10 and pain was categorized as mild, moderate, or severe. Data for 678 patients with pain at baseline were assessed and sensitivity analyses evaluated pain responses in the subgroup not taking concomitant pain medication (n = 384 at baseline). At Week 4 after the first injection, there was a mean change of −1.25 (standard deviation 2.04) points from baseline pain severity (p < 0.0001), with 48.1% showing ≥ 30% pain reduction from baseline, 34.4% showing ≥50% pain reduction from baseline, and 10.3% becoming pain free. Pain responses were sustained over five injection cycles with a trend to incremental improvements with each successive cycle. Pain responses in the subgroup not taking concomitant pain medication demonstrated the lack of confounding effects of pain medications. These results confirmed the pain relief benefits of long-term treatment with incoBoNT-A.

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 score0.201
Threshold uncertainty score0.577

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
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 head0.017
GPT teacher head0.271
Teacher spread0.254 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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