Impact of Completing FACE-Q Craniofacial Module Scales on Children and Young Adults with Facial Differences: An International Study
Bibliographic record
Abstract
BACKGROUND: The FACE-Q Craniofacial Module measures outcomes that matter to patients with diverse craniofacial conditions. However, it is not known whether completing a patient-reported outcome measure (PROM) has a negative impact on patients, particularly children. This study aims to investigate the impact of completing the FACE-Q Craniofacial Module and identify factors associated with a negative impact. METHODS: Participants were between 8 and 29 years of age, had a facial difference, and completed at least one module of the FACE-Q Craniofacial Module as part of the international field-test study between December of 2016 and 2019. Participants were asked three questions: "Did you like or dislike answering this questionnaire?" "Did answering these questions change how you feel about how you look?" and "Did answering this questionnaire make you feel unhappy or happy?" Univariate and multivariable logistic regression analyses were used to evaluate variables associated with a negative response. RESULTS: The sample included 927 participants. Most patients responded neutrally to all impact questions: 42.7% neither disliked nor liked the questionnaire; 76.6% felt the same about how they looked; and 72.7% felt neither unhappy nor happy after completion. Negative responses represented a small proportion of patients across all three impact questions (<13.2%). Increased craniofacial severity, more scales completed, and lower scores on all FACE-Q scales were associated with negative responses for all three impact questions ( P <0.01). CONCLUSIONS: This study provides evidence that the FACE-Q Craniofacial Module is acceptable for most participants. Clinicians and study investigators should follow up with patients after completing this PROM to address areas of concern in scale scores. CLINICAL QUESTION/LEVEL OF EVIDENCE: Risk, III.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".