Harmonisation of the 0-10 Numerical Rating Scale for pain intensity and the pain domain of the Western Ontario and McMaster Universities Osteoarthritis Index
Bibliographic record
Abstract
Abstract Background Analgesic efficacy is often evaluated with patient-reported outcome measures (PROMs). However, many different PROMs are used, which poses a problem when results are pooled in meta-analyses. Two commonly used PROMs used for evaluating pain intensity are the 0-10 numerical rating scale (NRS) and the pain domain of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Objective In this study, we aim to evaluate if the NRS and the WOMAC pain domain can be harmonised and, if possible, to report a conversion table. 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 ). We will apply multiple imputation to impute missing WOMAC pain domain responses. We will assess the distribution of scores and evaluate if NRS and the WOMAC pain domain have sufficient correlation and a monotonous relationship. We will also assess the NRS and WOMAC pain domain relationship within important subgroups, such as age, sex, and type of surgery. If deemed appropriate, we will perform equipercentile linking to create a conversion table from WOMAC pain domain sum scores to NRS scores and vice versa. We will assess the relationship between predicted and actual values with root mean squared errors (RMSEs) and mean absolute errors (MAEs), concordance correlation coefficients (CCCs) and intraclass correlation coefficients (ICC), Bland-Altman plots, residual plots, and calibration plots. Perspective The results will be submitted for publication in a peer-reviewed journal. 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 | 0.125 | 0.218 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".