Exploring the Role of Wideband Tympanometry in Diagnosis of Meniere’s Disease: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Objective: We aimed to investigate the diagnostic role of wideband tympanometry (WBT) in Meniere’s disease (MD). Methods: Scopus, PubMed, Web of Science, Cochrane Central Register of Control Trials, Google Scholars, Medline, and Embase were systematically searched up to March, 2024. The Preferred Reporting Items for Systematic Reviews and Meta-analyses and Problem/population, intervention, comparison, and outcome (PICO) guidelines were used. Two authors independently reviewed the eligible articles and assessed the quality of them using the Newcastle–Ottawa Scale for case–control studies. Ears diagnosed with MD were considered as affected ears, whereas ears without MD pathology in participants with unilateral MD were considered as unaffected ears. In addition, ears in healthy control groups with both ears without pathology of MD were considered as control ears. A meta-analysis was performed to compare the parameters of WBT between affected, unaffected, and control ears. Results: In total, 5 case–control studies including 446 ears met the inclusion criteria. Among 446 ears, 163, 81, and 202 were affected, unaffected, and control ears, respectively. There were not any statistically significant differences between affected, unaffected, and control ears for mean of resonance frequency (RF) ( P > 0.05). Furthermore, there were not any statistically significant differences between affected, unaffected, and control ears for mean of absorbance. Conclusion: WBT is not sensitive and specific enough to diagnose MD alone. That might be better to use WBT in conjunction with other objective tools, physical examination, and patient’s history instead of relying only on WBT.
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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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.039 |
| 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) | 0.003 | 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".