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Record W2407804127 · doi:10.20529/ijme.2013.007

Use of the WOMAC questionnaire in Mumbai and the challenges of translation and cross cultural adaptation

2013· article· en· W2407804127 on OpenAlexaboutno aff
Nitin Gogtay, U M Thatte, Biplab Dasgupta, S.N. Deshpande

Bibliographic record

VenueIndian Journal of Medical Ethics · 2013
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsWOMACContext (archaeology)WitnessAdaptation (eye)PopulationMedicineCross-culturalPhysical therapyPsychologyOsteoarthritisAlternative medicineSociologyPolitical scienceGeography

Abstract

fetched live from OpenAlex

Patient-reported outcome measures (PROMs) are disease specific questionnaires that are being increasingly used in clinical practice and research. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), is a widely used PROM in patients with hip or knee osteoarthritis. A validated WOMAC was used by us, and significant challenges were faced in administering it as several questions did not have a cultural connect. Functionally equivalent items in the Indian context had then to be used to complete the score. With greater emphasis today on the use of patient-reported outcome measures, and with data from multi-centric studies being pooled, cross-cultural adaptation becomes very important if the pooled data are to be really relevant. In India, with several languages being spoken, and a significant proportion of the population being illiterate, the physician and/ or the impartial witness must provide considerable explanation without attempting to influence the response. The key to the effective and correct use of PROMs thus lies not just in translation, but also in a stepwise validation of the questionnaire, and modification in the context of the country where it is used. Scores like WOMAC are often primary efficacy endpoints in clinical trials; are gaining greater importance to support label claims; have ethical implications, and directly impact regulatory decision making and thus, eventually, evidence-based practice.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.697
Threshold uncertainty score0.279

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.083
GPT teacher head0.341
Teacher spread0.258 · 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

Citations6
Published2013
Admission routes1
Has abstractyes

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