Psychometric Validation of the <scp>FACE</scp>‐Q Dental Module in Patients With Malocclusions
Bibliographic record
Abstract
OBJECTIVE: The FACE-Q Craniofacial Module is a patient-reported outcome measure (PROM) developed for children and young adults with craniofacial conditions. We hypothesised that some of its scales may be applicable to other populations. The aim of this study was to assess the validity and reliability of FACE-Q scales for patients with dental malocclusions. METHODS: The FACE-Q Dental Module includes 5 scales from the Craniofacial Module that measure appearance (Face, Jaws, Smile and Teeth) and function (Eating/Drinking). Data were collected from patients aged 8-29 years who presented with a dental malocclusion (pre-treatment) or 1-2 years after orthodontic treatment (post-treatment) at a large university-based orthodontic specialty clinic in Canada between September 2018 and March 2020. Patients completed a paper questionnaire booklet, and data were entered into a Research Electronic Data Capture (REDCap) survey. The psychometric analysis was performed using Rasch Measurement Theory (RMT) analysis. RESULTS: The sample of 434 patients was aged 9 to 29 years, with 249 female and 185 male participants. The sample included 252 pre-treatment and 182 post-treatment patients. The 4 appearance scales evidenced strong psychometric performance; all 37 items had ordered thresholds with good item fit to the Rasch model. Reliability was high, with person separation index and Cronbach alpha values, with and without extremes ≥ 0.86. As hypothesised, those participants who had a major difference in appearance, and those who reported liking their appearance less, scored lower on the appearance scales (p < 0.001). In the RMT analysis, the Eating/Drinking scale evidenced low reliability and poor targeting with close to 40% of particpants scoring at the ceiling. CONCLUSION: The FACE-Q Dental Module provides a means to collect evidence-based outcomes data from children and young adults who undergo orthodontic care for dental malocclusions.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".