Use of the WOMAC questionnaire in Mumbai and the challenges of translation and cross cultural adaptation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.128 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".