Impact of speeding on the validity of patient-reported outcome measures. An analysis of psychometric properties stratified by response times
Bibliographic record
Abstract
Abstract Background Randomised trials and meta-evidence increasingly rely on patient-reported outcome measures (PROMs). The psychometric properties of patient-reported outcome measures PROMs, including validity, reliability, and responsiveness, are typically established using high-quality datasets, which may not reflect the data quality in clinical trials. Increasing survey burden and fatigue may lead to issues with short response times that can reflect insufficient engagement. This phenomenon, often referred to as ‘speeding’ can lead to random, patterned, or otherwise invalid responses. Objective This study aims to investigate how response times influence the psychometric properties of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain domain in patients with chronic postsurgical pain. Methods The study is based on responses to the 5-item Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain domain (Likert-scale, version 3.1) from 2,031 patients who underwent total hip arthroplasty (THA), 2,172 patients who underwent total knee arthroplasty (TKA), and 870 patients who underwent unicompartmental knee arthroplasty (UKA) more than one year previously. Each of the three datasets, containing patients who have undergone THA, UKA and TKA, are individually stratified into response time deciles. For each of the deciles, 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 will be submitted for publication in a peer-reviewed journal. We will seek to make the report 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 | 0.652 | 0.736 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.048 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".