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Record W4414584603 · doi:10.7860/jcdr/2025/80478.21654

Hindi Translation, Cross-cultural Adaptation and Validation of Chedoke McMaster Stroke Assessment (CMSA) Scale: A Cross-sectional Study

2025· article· en· W4414584603 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJOURNAL OF CLINICAL AND DIAGNOSTIC RESEARCH · 2025
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
Fundersnot available
KeywordsHindiContent validityRehabilitationRelevance (law)Delphi methodAdaptation (eye)DelphiConsistency (knowledge bases)

Abstract

fetched live from OpenAlex

Introduction: Stroke is one of the leading causes of death and long-term disability worldwide, emphasising the need for effective rehabilitation strategies. The Chedoke McMaster Stroke Assessment (CMSA) is a reliable and valid measure developed in Canada used to assess both impairment and activity levels in persons with stroke. The widespread use of Hindi, there is no Hindi translation of the CMSA. Developing a culturally and linguistically appropriate version of the CMSA for Hindi speakers could enable rehabilitation personnel to evaluate change in the patient’s motor control and functional ability. Aim: This study focussed on translating and adapting the CMSA into Hindi to ensure its relevance and effectiveness for assessing stroke recovery for patients in India by Hindi-speaking rehabilitation specialists. Materials and Methods: We obtained permission from the original author of the CMSA to translate the tool into Hindi. The translation process adhered to recognise guidelines for crosscultural adaptation. Two bilingual experts, one with a medical background and the other a linguistic specialist, independently translated the CMSA into Hindi. The translations were combined and back-translated into English by independent translators to ensure consistency with the original tool. To ensure content validity, we used the Delphi method to assess the relevance of each item in the scale. The experts evaluated each item on a 4-point scale, and the Item-Level Content Validity Index (I-CVI) and Scale-Level Content Validity Index Average (S-CVI/Ave) were calculated. Results: There is an evidence of its criterion validity which demonstrated it as high degree of linguistic and cultural equivalence. The Hindi CMSA achieved an I-CVI of 0.98985, an S-CVI/Ave of 0.98985, and an S-CVI/UA of 0.881944, indicating strong evidence of its validity. Conclusion: The Hindi CMSA has been culturally adapted and validated for evaluating stroke-related impairments and functional activity in Hindi-speaking healthcare environments. This version will enhance the ability of rehabilitation personnel in conducting clinical assessments and customising rehabilitation strategies for this population.

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.

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)0.006
metaresearch head score (Gemma)0.007
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 score0.018
Threshold uncertainty score0.831

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.222
GPT teacher head0.550
Teacher spread0.328 · 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