FACE-Q Skin Cancer Module for measuring patient-reported outcomes following facial skin cancer surgery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it