Biomarkers for Inner Ear Disorders: Scoping Review on the Role of Biomarkers in Hearing and Balance Disorders
Bibliographic record
Abstract
The diagnostics of inner ear diseases are primarily functional, but there is a growing interest in inner ear biomarkers. The present scoping review aimed to elucidate gaps in the literature regarding the definition, classification system, and an overview of the potential uses of inner ear biomarkers. Relevant biomarkers were categorized, and their possible benefits were evaluated. The databases OVID Medline, EMBASE, EBSCO COINAHL, CA PLUS, WOS BIOSIS, WOS Core Collection, Proquest Dissertations, Theses Global, PROSPERO, Cochrane Library, and BASE were searched using the keywords "biomarker" and "inner ear". Of the initially identified 1502 studies, 34 met the inclusion criteria. The identified biomarkers were classified into diagnostic, prognostic, therapeutic, and pathognomonic; many were detected only in the inner ear or temporal bone. The inner-ear-specific biomarkers detected in peripheral blood included otolin-1, prestin, and matrilin-1. Various serum antibodies correlated with inner ear diseases (e.g., anti-type II collagen, antinuclear antibodies, antibodies against cytomegalovirus). Further studies are advised to elucidate the clinical significance and diagnostic or prognostic usage of peripheral biomarkers for inner ear disorders, filling in the literature gaps with biomarkers pertinent to the otology clinical practice and integrating functional and molecular biomarkers. These may be the building blocks toward a well-structured guideline for diagnosing and managing some audio-vestibular disorders.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".