Administering selected subscales of patient-reported outcome questionnaires to reduce patient burden and increase relevance: a position statement on a modular approach
Bibliographic record
Abstract
Patient-reported outcome (PRO) questionnaires considered in this paper contain multiple subscales, although not all subscales are equally relevant for administration in all target patient populations. A group of measurement experts, developers, license holders, and other scientific-, regulatory-, payer-, and patient-focused stakeholders participated in a panel to discuss the benefits and challenges of a modular approach, defined here as administering a subset of subscales out of a multi-scaled PRO measure. This paper supports the position that it is acceptable, and sometimes preferable, to take a modular approach when administering PRO questionnaires, provided that certain conditions have been met and a rigorous selection process performed. Based on the experiences and perspectives of all stakeholders, using a modular approach can reduce patient burden and increase the relevancy of the items administered, and thereby improve measurement precision and eliminate wasted data without sacrificing the scientific validity and utility of the instrument. The panelists agreed that implementing a modular approach is not expected to have a meaningful impact on item responses, subscale scores, variability, reliability, validity, and effect size estimates; however, collecting additional evidence for the impact of context may be desirable. It is also important to recognize that adequate rationale and evidence (e.g., of fit-for-purpose status and relevance to patients) and a robust consensus process that includes patient perspectives are required to inform selection of subscales, as in any other measurement circumstance, is expected. We believe that the considerations discussed within (content validity, administration context, and psychometric factors) are relevant across multiple therapeutic areas.
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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 | 0.316 | 0.300 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".