The HAND-Q: Psychometrics of a New Patient-reported Outcome Measure for Clinical and Research Applications
Bibliographic record
Abstract
The perspective of the patient in measuring the outcome of their hand treatment is of key importance. We developed a hand-specific patient-reported outcome measure to provide a means to measure outcomes and experiences of care from the patient perspective, that is, HAND-Q. METHODS: Data were collected from people with a broad range of hand conditions in hand clinics in six countries between April 2018 and January 2021. Rasch measurement theory analysis was used to perform item reduction and to examine reliability and validity of each HAND-Q scale. RESULTS: A sample of 1277 patients was recruited. Participants ranged in age from 16 to 89 years, 54% were women, and a broad range of congenital and acquired hand conditions were represented. Rasch measurement theory analysis led to the refinement of 14 independently functioning scales that measure hand appearance, health-related quality of life, experience of care, and treatment outcome. Each scale evidenced reliability and validity. Examination of differential item functioning by age, gender, language, and type of hand condition (ie, nontraumatic versus traumatic) confirmed that a common scoring algorithm for each scale could be implemented. CONCLUSIONS: The HAND-Q was developed following robust psychometric methods to provide a comprehensive modular independently functioning set of scales. HAND-Q scales can be used to assess and compare evidence-based outcomes in patients with any type of hand condition.
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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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".