Guidelines for the development and validation of patient-reported outcome measures: a scoping review
Bibliographic record
Abstract
Objective The objectives of this scoping review were to provide an overview of existing guidelines for the development and validation of patient-reported outcome measures (PROMs), review them for comprehensiveness and clarity and provide recommendations for their use based on the goals of the instrument developers. Design Scoping review. Methods A literature search was performed in PubMed, Scopus, PsycInfo and Google Scholar up to 2 June 2023 to identify guidelines for the development and validation of PROMs. Screening of records and reports as well as data extraction were performed by two reviewers. To assess the comprehensiveness of the included guidelines, a mapping synthesis was performed and steps to develop and validate a measurement instrument outlined in the included guidelines were mapped to an a priori framework including 20 steps, which was based on the guideline by de Vet et al . Results A total of 40 guidelines were included. Statistical advice (at least partially) was provided in 98% of the guidelines (39/40) and 88% (35/40) of the guidelines included examples for steps required to develop and validate PROMs. However, 78% (31/40) of the guidelines were not comprehensive and two essential steps in PROM development (‘consideration and elaboration of the measurement model’ and ‘responsiveness’) were not included in 80% and 72% of the guidelines, respectively. Three guidelines included all 20 steps and six included almost all steps (≥90% of steps) for developing and validating a PROM. Discussion Most guidelines on PROM development and validation do not appear to be comprehensive, and some crucial steps are missing in most guidelines. Nevertheless, for some purposes of PROMs, many guidelines provide helpful advice and support. Conclusion At least 15 guidelines may be recommended, including three comprehensive guidelines that can be recommended for the development and validation of PROMs for most purposes (eg, to discriminate between subjects with a particular condition and subjects without that condition, to evaluate the effects of treatments (between a pre and post time-points) or to evaluate a status quo).
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.333 | 0.587 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.018 |
| Bibliometrics | 0.064 | 0.045 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.012 | 0.012 |
| Research integrity | 0.015 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".