The case for an international patient-reported outcomes measurement information system (PROMIS®) initiative
Bibliographic record
Abstract
Patient-reported outcomes (PROs) play an increasingly important role in clinical practice and research. Modern psychometric methods such as item response theory (IRT) enable the creation of item banks that support fixed-length forms as well as computerized adaptive testing (CAT), often resulting in improved measurement precision and responsiveness. Here we describe and discuss the case for developing an international core set of PROs building from the US PROMIS® network.PROMIS is a U.S.-based cooperative group of research sites and centers of excellence convened to develop and standardize PRO measures across studies and settings. If extended to a global collaboration, PROMIS has the potential to transform PRO measurement by creating a shared, unifying terminology and metric for reporting of common symptoms and functional life domains. Extending a common set of standardized PRO measures to the international community offers great potential for improving patient-centered research, clinical trials reporting, population monitoring, and health care worldwide. Benefits of such standardization include the possibility of: international syntheses (such as meta-analyses) of research findings; international population monitoring and policy development; health services administrators and planners access to relevant information on the populations they serve; better assessment and monitoring of patients by providers; and improved shared decision making.The goal of the current PROMIS International initiative is to ensure that item banks are translated and culturally adapted for use in adults and children in as many countries as possible. The process includes 3 key steps: translation/cultural adaptation, calibration, and validation. A universal translation, an approach focusing on commonalities, rather than differences across versions developed in regions or countries speaking the same language, is proposed to ensure conceptual equivalence for all items. International item calibration using nationally representative samples of adults and children within countries is essential to demonstrate that all items possess expected strong measurement properties. Finally, it is important to demonstrate that the PROMIS measures are valid, reliable and responsive to change when used in an international context.IRT item banking will allow for tailoring within countries and facilitate growth and evolution of PROs through contributions from the international measurement community. A number of opportunities and challenges of international development of PROs item banks are discussed.
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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 | 0.552 | 0.495 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.024 | 0.049 |
| Open science | 0.012 | 0.031 |
| Research integrity | 0.035 | 0.087 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".