Development of an Item Bank for a Health-Related Quality of Life Measure in Spondyloarthritis
Bibliographic record
Abstract
OBJECTIVE: Health-related quality of life (HRQOL) is an important aspect in the management of chronic diseases such as spondyloarthritis (SpA). A promising approach to reduce respondent burden when measuring HRQOL is the use of shorter patient-reported outcome measures (PROMs) delivered using computerized adaptive tests (CATs). However, the lack of an item bank that covers the entire continuum of the HRQOL domain impedes the development of CATs to measure HRQOL among patients with SpA. We aimed to develop an item bank for an HRQOL measure among patients with SpA based on the items from existing validated PROMs. METHODS: This study is guided by the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) and Patient Reported Outcomes Measurement Information System (PROMIS) standards. Relevant articles were retrieved from PubMed, Embase, and PsycINFO (Ovid) databases. Items from existing PROMs were binned and winnowed according to the facets of HRQOL in the World Health Organization (WHO) quality of life framework. RESULTS: We identified 147 relevant articles, from which written permission was obtained for including 31 PROMs into the item bank. PROMs contained 1039 items, which underwent binning and winnowing. This resulted in 968 items covering 23 domains of HRQOL in the WHO framework, with the number of items within each domain ranging from 1 to 453. CONCLUSION: We created an item bank to measure HRQOL among patients with SpA using items from validated PROMs. This set can provide the foundation for the development of CATs to measure HRQOL among patients with SpA.
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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 | 0.057 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".