Systematic reviews of patient-reported outcome measures (PROMs): table templates for effective communication
Bibliographic record
Abstract
PURPOSE: Systematic reviews of outcome measurement instruments (OMIs) are an important tool to guide the selection of OMIs for research and clinical practice. However, presenting the large amount of complex data pertaining both to the quality of each study (i.e., risk of bias) as well as the quality of the instrument (i.e., measurement properties), along with the underpinning certainty of evidence, is challenging. Here, we aim to provide guidance on optimizing data presentation in OMI systematic reviews, specifically focusing on patient-reported outcome measures (PROMs). METHODS: A multidisciplinary team of experts in OMI systematic reviews, research reporting, and data visualization built on existing table templates from OMERACT and the COSMIN initiative, to align with reporting items in a recently developed reporting guideline for systematic reviews of OMIs: PRISMA-COSMIN for OMIs 2024. To enhance clarity and usability, we applied data visualization principles by reducing non-essential elements and improving interpretability through structured layouts and concise explanatory text. RESULTS: We present eight templates for reporting PROM systematic review results: three pertain to PROM characteristics, two to studies' characteristics, two to the evaluation of measurement properties, and one to the summary of findings. We also provide recommendations on whether to include these templates in the review's main manuscript or in the supplementary materials. Word versions of these templates can be downloaded from www.prisma-cosmin.ca and www.cosmin.nl . CONCLUSION: Templates complementing the PRISMA-COSMIN for OMIs 2024 reporting guidance can be used to standardize and enhance the clarity and usefulness of OMI systematic reviews focusing on PROMs. They comprise a comprehensive set of tools to effectively report OMI systematic reviews, in service of end-users who are selecting OMIs.
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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 | 0.195 | 0.576 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.024 | 0.027 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.067 | 0.021 |
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".