Hearing outcome measures for conductive and mixed hearing loss treatment in adults: a scoping review
Bibliographic record
Abstract
OBJECTIVE: Rehabilitation options for conductive and mixed hearing loss are continually expanding, but without standard outcome measures comparison between different treatments is difficult. To meaningfully inform clinicians and patients core outcome sets (COS), determined via a recognised methodology, are needed. Following our previous work that identified hearing, physical, economic and psychosocial as core areas of a future COS, the AURONET group reviewed hearing outcome measures used in existing literature and assigned them into different domains within the hearing core area. DESIGN: Scoping review. STUDY SAMPLE: Literature including hearing outcome measurements for the treatment of conductive and/or mixed hearing loss. RESULTS: The literature search identified 1434 studies, with 278 subsequently selected for inclusion. A total of 837 hearing outcome measures were reported and grouped into nine domains. The largest domain constituted pure-tone threshold measurements accounting for 65% of the total outcome measures extracted, followed by the domains of speech testing (20%) and questionnaires (9%). Studies of hearing implants more commonly included speech tests or hearing questionnaires compared with studies of middle ear surgery. CONCLUSIONS: A wide range of outcome measures are currently used, highlighting the importance of developing a COS to inform individual practice and reporting in trials/research.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".