Assessment of psychometric validity and cross-cultural differential item functioning of the Doleur Neuropatique en 4 questions interview and the pain domain of the Western Ontario and McMaster Universities Osteoarthritis Index
Bibliographic record
Abstract
Abstract Background Randomised trials and meta-evidence increasingly rely on patient-reported outcome measures (PROMs). Many PROMs are applied in languages and settings that differ from the original target population. However, translation of PROMs poses a threat to their construct validity, including issues with cross-cultural adaptation Objective This study aims to assess the psychometric validity of the Danish versions of the WOMAC pain domain and the DN4i, including cross-cultural differential item functioning. Methods The study is based on a large dataset of 12-to 18-month pain outcomes after primary total hip arthroplasty (THA), total knee arthroplasty (TKA), or unicompartmental knee arthroplasty (UKA) for osteoarthritis ( ClinicalTrials.gov identifiers NCT05845177 and NCT05900791 ). In addition to this dataset, we will apply for international data in similar populations to assess the cross-cultural differential item functioning. The assessed instruments are the 5-item Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain domain (Likert-scale, version 3.1) and the 7-item Doleur Neuropatique en 4 questions interview (DN4i) (Danish translation from www.mapi-trust.org ). We will evaluate if the data fit a congeneric measurement model, i.e. a model that assumes that the set of observed items all measure the same underlying latent factor. This evaluation of construct validity is done using Item Response Theory (IRT) and Confirmatory Factor Analysis (CFA). Perspective The results for WOMAC pain domain and DN4i will be reported in two separate reports, which are submitted for publication in peer-reviewed journals. We will seek to make the reports freely available, either by open-access publication or through publication on a preprint server, e.g. www.medrxiv.org .
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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 | 0.163 | 0.238 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".