Digging into the Role of Inflammatory Biomarkers in Sudden Sensorineural Hearing Loss Diagnosis and Prognosis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background and Objectives: Sudden Sensorineural Hearing Loss (SSNHL) is a quite common clinical finding in otolaryngology. Most cases are classified as idiopathic and there is a dearth of information on factors able to predict the response to treatment and hearing recovery. The main aim of this systematic review and meta-analysis was to assess and critically discuss the role of circulating inflammatory biomarkers in SSNHL. Materials and Methods: A search was conducted of the English literature published between 1 January 2009 and 7 July 2022 on Pubmed, Scopus, Web of Science, ScienceDirect, and Cochrane following PRISMA guidelines. Results: A total of 256 titles were retrieved from the search. After full-text screening and application of inclusion/exclusion criteria, 13 articles were included. Twelve out of thirteen studies reported significant differences in biomarkers values in SSNHL patients, of which Tumor Necrosis Factor alpha (TNF-α) and C-reactive Protein (CRP) were the most analyzed. Our meta-analysis for CRP’s mean values in SSNHL groups vs. controls showed significantly higher CRP levels with a pooled overall difference of 1.07; confidence interval (CI) at 95%: 0.03; 2.11. For TNF-α, discordant results were found: three studies showed significantly higher levels in SSNHL patients vs. controls, whereas other three investigations showed lower levels in the SSNHL groups (overall pooled difference 1.97; 95% CI: −0.90; 4.84). A high between-study heterogeneity was found. Conclusions: This systematic review pointed out that, although there exists a growing literature in the field of circulatory biomarkers identification in SSNHL, there is a high heterogeneity of results and low quality of evidence. CRP resulted to be higher in SSNHL patients than in controls, while TNF-α showed more heterogeneous behavior. The data reported herein needs to be confirmed in well-designed prospective multicenter randomized studies, with the objective of improving SSNHL treatment and outcome and thereby reducing the social burden of hearing loss.
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 | 0.027 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.048 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".