Clinical Significance Unveiled: Understanding the Meaning of FACE‐Q Skin Cancer Scores for Improved Patient Care
Bibliographic record
Abstract
OBJECTIVE: The FACE-Q Skin Cancer Module is a Patient-Reported Outcome Measure (PROM) utilized to assess outcomes following facial skin cancer resection. However, the lack of Minimal Important Difference (MID) estimates hinders the interpretability of the PROM scores. This study established MID estimates for the four outcome scales from the FACE-Q Skin Cancer Module using distribution-based methods. METHODS: A prospective cohort study at four hospitals in the United States, enrolled participants who underwent Mohs Micrographic Surgery (MMS) for facial skin cancer between April 2020 and April 2022. Participants completed the Satisfaction with Facial Appearance, Appearance-related Psychosocial Distress, Cancer Worry, and Appraisal of Scars scales at four time points: pre-operatively, 2-week, 6-month, and 1-year post-surgery. RESULTS: A total of 990 patients participated in the study, with completion rates of 98.4% for the pre-operative assessment, 70.8% at 2 weeks, 59.3% at 6 months, and 60.4% at 1 year. MID estimates, calculated using 0.2 standard deviation and 0.2 standardized response mean, were determined for the four scales. The mean MID estimates, based on a Rasch transformed score ranging from 0 to 100, were 5 for the Appraisal of Scars scale and 4 for the remaining three scales. CONCLUSION: This multicenter study provides valuable MID estimates for the FACE-Q Skin Cancer Module, specifically for the MMS patient population, enabling clinicians and researchers to better interpret scores, determine appropriate sample sizes, and apply the findings in clinical care.
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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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".