Development of a New Patient-reported Outcome Measure for Ear Conditions: The EAR-Q
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcome measures are widely used to improve health services and patient outcomes. The aim of our study was to describe the development of 2 ear-specific scales designed to measure outcomes important to children and young adults with ear conditions, such as microtia and prominent ears. METHODS: We used an interpretive description qualitative approach. Semi-structured qualitative and cognitive interviews were performed with participants with any type of ear condition recruited from plastic surgery clinics in Canada, Australia, United States, and United Kingdom. Participants were interviewed to elicit new concepts. Interviews were audio-recorded, transcribed, and coded using the constant comparison approach. Experts in ear reconstruction were invited to provide input via an online Research Electronic Data Capture survey. RESULTS: Participants included 25 patients aged 8-21 years with prominent ears (n = 9), microtia (n = 14), or another condition that affected ear appearance (n = 2). Analysis of participant qualitative data, followed by cognitive interviews and expert input, led to the development and refinement of an 18-item ear appearance scale (eg, size, shape, look up close, look in photographs) and a 12-item adverse effects scale (eg, itchy, painful, numb). CONCLUSIONS: The EAR-Q in currently being field-tested internationally. Once finalized, we anticipate the EAR-Q will be used in clinical practice and research to understand the patient's perspective of outcomes following ear surgery.
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 | 0.045 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".