Patient factors associated with novel EAR-Q appearance, psychosocial, and social scales: A cross-sectional study and regression analysis
Bibliographic record
Abstract
INTRODUCTION: The EAR-Q is a rigorously validated patient-reported outcome measure, which evaluates ear appearance and health-related quality of life (HRQL) in patients with congenital or acquired ear conditions. The aim of this study was to conduct an exploratory analysis to examine the factors associated with EAR-Q appearance and HRQL scale scores. METHODS: In this study, 862 participants, aged 8-29 years, with congenital or acquired ear conditions, completed the EAR-Q as part of an international field-test study. Patients responded to demographic and clinical questions as well as the EAR-Q. Univariable and multivariable linear regression analyses were used to determine factors that were significant predictors for the scores on the EAR-Q Appearance, Psychological, and Social scales. RESULTS: Most participants were men (57.4%), awaiting treatment (55.0%), and had a microtia diagnosis (70.4%), with a mean age of 13 (±4) years. Worse ear appearance scores (p < 0.02) were associated with male gender, microtia, no history of treatment, ear surgery within 6 months, unilateral involvement, and greater self-reported ear asymmetry. Decreased psychological scores (p < 0.01) were associated with increasing participant age, no treatment history, recent ear surgery, and dissatisfaction with ears matching or overall dissatisfaction. Lower social scores (p ≤ 0.04) were associated with no treatment history, those awaiting surgery, ear surgery within the last 6 months, bilateral involvement, and self-reported ears matching or overall appearance. CONCLUSION: This analysis identified patient factors that may influence ear appearance and HRQL scale scores. These findings provide evidence of patient factors that should be adjusted for when undertaking future observational research designs using the EAR-Q in this patient population.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".