Measurement Properties of the Hearing Environments and Reflection of Quality of Life (HEAR-QL) 28-item Questionnaire in Cholesteatoma
Bibliographic record
Abstract
OBJECTIVE: We sought to assess the reliability and construct validity of the Hearing Environments and Reflection on Quality of Life Adolescent (HEAR-QL 28) quality of life measure (QoL) in cholesteatoma. STUDY DESIGN: Observational. SETTING: Tertiary referral center. PATIENTS: One hundred seventeen patients with a diagnosis or history of cholesteatoma completed HEAR-QL 28. In addition to patients within the age range recommended for HEAR-QL 28 (13-18 yr), patients under 13 years old who were able to complete HEAR-QL 28 without parental assistance were included. INTERVENTION S: Completion of HEAR-QL 28 QoL measure. MAIN OUTCOME MEASURE S: HEAR-QL 28 score, four tone average pure tone audiogram hearing threshold and categorical classification of hearing loss as mild, moderate, and severe. RESULTS: HEAR-QL 28 did not demonstrate discriminative ability on the basis of audiometric threshold, but did discriminate between participants hearing normally (four tone average pure tone audiogram <30 dB HL) (HEAR-QL 86/100) after cholesteatoma surgery from those with unilateral hearing loss (HEAR-QL 73/100) (p < 0.001). Those with unilateral loss could in turn be differentiated from those with bilateral loss (HEAR-QL 60/100) (p < 0.006). CONCLUSION: HEAR-QL 28 is valid measure of QoL in cholesteatoma with no evidence of redundancy and excellent internal consistency. The importance of considering QoL impact of cholesteatoma is highlighted by 17% of participants reporting the normality or abnormality of their hearing differently from their audiometric threshold. The HEAR-QL 28 provides insight into the ability to cope with their hearing environment in a specific environment.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".