FACE-Q Skin Cancer Module for measuring patient-reported outcomes following facial skin cancer surgery
Bibliographic record
Abstract
BACKGROUND: The patient's perspective of their facial scar after skin cancer surgery influences perception of care and quality of life (QoL). Appearance satisfaction after surgery is also an important but often overlooked treatment outcome. OBJECTIVES: To report the psychometric validation of the FACE-Q Skin Cancer Module consisting of five scales, measuring appearance satisfaction (Satisfaction with Facial Appearance, Appraisal of Scars), QoL (Cancer Worry, Appearance-related Psychosocial Distress) and the patient experience (Satisfaction with Information: Appearance). METHODS: Participants underwent Mohs surgery for facial basal or squamous cell carcinoma or excision of early facial melanoma. Cohort 1 received a set of scales before and after surgery. Cohort 2 received the scales on two occasions in the postoperative period for test-retest reliability. Rasch measurement theory was used to select (item-reduce) the most clinically meaningful items for the scales. Reliability, validity, floor and ceiling effects and responsiveness were also analysed. RESULTS: Of 334 patients, 209 (response rate 62·6%) were included. Rasch analysis reduced the total scale items from 77 to 41. All items had ordered thresholds and good psychometric fit. Reliability was high (Person separation index and Cronbach's α ≥ 0·90) and scales measuring similar constructs were correlated. High floor and ceiling effects were seen for the scales. The Cancer Worry scale demonstrated responsiveness (P = 0·004). CONCLUSIONS: The FACE-Q Skin Cancer Module meet the requirements of the Rasch model providing linearized measurement. Discriminating between patients with minimal appearance or worry impairment may be a limitation. The scales can be used for larger validation studies, clinical practice 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".