Development and validation of a patient‐reported outcome measure for hair loss treatment: The <scp>HAIR</scp>‐Q
Bibliographic record
Abstract
Abstract Background Patient‐reported outcome measures (PROMs) for hair loss focus mainly on Alopecia Areata. We created a PROM (i.e., HAIR‐Q) that is applicable to any hair loss condition. The HAIR‐Q measures satisfaction with hair. Patients/Methods Concept elicitation interviews were conducted and analyzed to develop a draft scale. Content validity was established through multiple rounds of patient and expert input. Psychometric properties of the scale were examined in an online sample (i.e., Prolific) using Rasch measurement theory (RMT) analysis. Test–retest reliability and tests of construct validation were examined. Results Content validity of a 22‐item draft scale was established with input from 11 patients, 12 experts and an online Prolific sample of 59 people who had a variety of hair loss treatments. In the RMT analysis (n = 390), 8 items were dropped. Data for the 14‐item scale fit the Rasch model (χ2 = 89.85, df = 70, p = 0.06). All 14 items had ordered thresholds and good item fit. Reliability was high with person separation index and Cronbach alpha values ≥0.91, and intraclass correlation coefficient of 0.94 based on a sample of 97 participants. Higher (better) scores on the scale were associated with having more hair, looking younger than ones' age, satisfaction with hair overall, being less bothered by hair loss, and for those who had a hair loss treatment in the past year, being more satisfied with their hair now than before treatment (p < 0.001). Conclusion The HAIR‐Q evidenced reliability and validity and can be used in research and to inform clinical care to measure satisfaction with hair from the patient perspective.
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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 | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".