Establishing Reliability and Validity of the FACE-Q Craniofacial Module for Pediatric Head and Neck Cancer
Bibliographic record
Abstract
Purpose: We aimed to establish content validity and assess the psychometric properties of the FACE-Q Craniofacial Module, a patient-reported outcome measure, for use in pediatric and adolescent patients with head and neck cancer (HNC). Methods: To establish content validity (Part 1), between June 2017 and August 2019, cognitive interviews were conducted with survivors of pediatric HNC ( n = 15), and input was obtained from clinical experts ( n = 21). To examine item and scale performance (Part 2), Rasch Measurement Theory (RMT) analysis was performed using data from two international studies ( n = 121). Results: Part 1: Qualitative data from 15 survivors and input from 21 experts provided evidence to support the use of the FACE-Q Craniofacial Module in pediatric HNC. Part 2: The field-test study sample included 121 survivors of pediatric HNC. RMT analysis provided evidence of reliability and validity for 10 FACE-Q scales. Data for each scale fit the RMT model. Scale reliability was high, with Person Separation Index and Cronbach's alpha values ≥0.82 for 9 scales. Mean scores on the Appearance, Psychological, and Social scales were higher for those who liked aspects of their face more. For participants with (vs. without) a facial difference, mean scores were lower for the Face, Jaws, Psychological, and Social scales. Conclusion: The FACE-Q Craniofacial Module evidenced reliability and validity for HNC survivors aged 8–29 years and can be used in research and clinical care to measure quality of life of pediatric survivors with HNC.
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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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".