Establishing content validity of an orthodontic subset of the FACE‐Q Craniofacial Module in children and young adults with malocclusion
Bibliographic record
Abstract
OBJECTIVE: The FACE-Q Craniofacial Module for children and young adults is a patient-reported outcome measure (PROM) designed to measure outcomes for patients aged 8 to 29 years with facial conditions. The aim of this study was to establish content validity of a relevant subset of the module for its use in orthodontic patients with malocclusion. SETTING AND SAMPLE POPULATION: Experts in orthodontics were emailed and invited to provide feedback through a Research Electronic Data Capture survey. Patient feedback was obtained through cognitive interviews with patients aged 8 to 29 years recruited from a university-based orthodontic clinic in Canada. MATERIALS AND METHODS: Expert opinion and patient interviews were used to obtain feedback on the content of 4 appearance (face, smile, teeth and jaws) and 1 function (eating and drinking) scales hypothesized to be relevant to orthodontic malocclusions, and to elicit new concepts. Interviews were audio-recorded, transcribed, and coded using a line-by-line approach. RESULTS: Twenty-one experts and 15 patients participated. Expert feedback led us to drop 9, retain 40, revise 4 and add 16 new items. At the conclusion of cognitive interviews no items were dropped, 55 were retained, 5 were revised and 8 new items were added. The final set of 68 items demonstrated content validity for orthodontic patients. CONCLUSION: Expert feedback and cognitive interviews enabled us to revise and refine 5 scales as part of the FACE-Q Craniofacial Module for use in orthodontic patients. These scales were included in the internationalfield-test of the FACE-Q Craniofacial Module.
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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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| 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".