Development of a New Module of the FACE-Q for Children and Young Adults with Diverse Conditions Associated with Visible and/or Functional Facial Differences
Bibliographic record
Abstract
Abstract Appearance and facial function are concepts not well addressed in current pediatric patient-reported outcome measures (PROM) for facial conditions. We aimed to develop a new module of the FACE-Q for children/young adults with facial conditions that include ear anomalies, facial paralysis, skeletal conditions, and soft tissue conditions. Semi-structured and cognitive interviews were conducted with patients aged 8–29 years recruited from craniofacial centers in Canada, USA, UK, and Australia. Interviews were used to elicit new concepts and to obtain feedback on CLEFT-Q scales hypothesized to be relevant to other facial conditions. Interview data were recorded, transcribed, and coded. Experts were emailed and invited to provide feedback via Research Electronic Data Capture (REDCap). Eighty-four participants and 43 experts contributed. Analysis led to the development of a conceptual framework and 14 new scales that measure appearance, facial function, health-related quality of life, and adverse effects of treatment. In addition, 12 CLEFT-Q scales were determined to have content validity for use with other facial conditions. Expert input led to minor changes to scales and items. This new FACE-Q module for children/young adults is being field-tested internationally. Once finalized, we anticipate this PROM will be used to inform clinical practice and research studies.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".