Psychometric Validation of the FACE-Q Craniofacial Module for Facial Nerve Paralysis
Bibliographic record
Abstract
Background: Systematic reviews have identified the need for a patient-reported outcome measure for facial nerve paralysis (FNP). The aim of this study was to determine the psychometric properties of FACE-Q Craniofacial module scales when used in a combined sample of children and older adults with FNP. Methods: Data were collected between December 2016 and December 2019. We conducted qualitative interviews with children and adults with FNP. FACE-Q data were collected from patients aged 8 years and older with FNP. Rasch measurement theory analysis was used to examine the reliability and validity of the relevant scales in the FNP sample. Results: Twenty-five patients provided 2052 qualitative codes related to appearance, physical, psychological, and social function. Many patient concerns were common across age. The field-test sample included 235 patients aged 8–81 years. Of the 13 scales examined, all 122 items had ordered thresholds and good item fit to the Rasch model. For 12 scales, person separation index values were ≥0.79 and Cronbach's alpha values were ≥0.82. The 13th scale's reliability values were ≥0.71. Conclusion: The FACE-Q Craniofacial module scales described in this study can be used to collect and compare evidence-based outcome data from children and adults with FNP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".