Determining Construct Validity of a Patient-Reported Outcome Measure for Birthmarks on the Face and Body
Bibliographic record
Abstract
Background: The FACE-Q Craniofacial module includes a scale that measures how bothered an individual is by the appearance of a birthmark on the face or body. Objective: To determine if the Birthmark scale measuring appearance of the birthmark has evidence of construct validity among children and young adults, aged 8–29 years old, with a birthmark on the face or body. Methods: Participants were recruited as part of the field test of the FACE-Q Craniofacial module. Construct validity of the Birthmark scale was examined using a priori hypotheses testing. Results: Two hundred seventy participants were included, who were predominantly female (60.4%) and had a facial birthmark (71.5%). The Birthmark scale correlated ( p ≤ 0.01) with scale scores for Face, Appearance Distress, Psychological, School, and Social. Scores for participants with more “noticeable” birthmarks were ( p ≤ 0.01) associated with worse Birthmark scale scores. Conclusion: The findings support that the Birthmark scale can be used to measure the patient's perspective of the appearance of their birthmark, providing a means for clinicians to incorporate the patient's view in shared decision-making and research.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".