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Record W4403214780 · doi:10.1002/mdc3.14222

Interdisciplinary Consensus in Evaluating the Severity Subscale of the Original and Revised Toronto Western Spasmodic Torticollis Rating Scale Through Video‐Based Assessment: An Inter‐Rater Reliability Study

2024· article· en· W4403214780 on OpenAlexaboutno aff
Shimelis Girma Kassaye, Joke De Pauw, Ségolène De Waele, Willem De Hertogh, Esayas Kebede Gudina, David Crosiers

Bibliographic record

VenueMovement Disorders Clinical Practice · 2024
Typearticle
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsSpasmodic TorticollisRating scaleInter-rater reliabilityPsychologyPhysical medicine and rehabilitationReliability (semiconductor)Physical therapyClinical psychologyPsychiatryDystoniaMedicineDevelopmental psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) is widely employed for cervical dystonia (CD) evaluation. OBJECTIVE: To assess the inter-rater reliability of the severity subscale of the original and revised TWSTRS using video recordings. METHODS: Three raters, a PhD student with a nursing degree, a physiotherapist specialized in CD, and a neurologist-in-training independently rated all videos. The inter-rater reliability was assessed with the intra-class correlation coefficient (ICC). RESULTS: The total severity score of both tools demonstrated a good inter-rater reliability (ICC = 0.87 to 0.88). The inter-rater reliability of individual sub-items varied from poor (ICC = 0.29) to excellent (ICC = 0.9). CONCLUSIONS: The total severity score of both TWSTRS showed good inter-rater reliability in a multidisciplinary team, indicating their applicability for online patients' assessment. We recommend using the total subscale for outcome comparison. Furthermore, there is a need for more accurate definitions of duration factor and shoulder elevation.

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)0.005
metaresearch head score (Gemma)0.002
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 score0.078
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.065
GPT teacher head0.478
Teacher spread0.413 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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