Exploring the Prevalence of Psychiatric Disorders in Otosclerosis Patients: A Systematic Review
Bibliographic record
Abstract
Objective: The primary objective of this research is to conduct a systematic review of the available studies and evidence to determine if there is a significant relationship between otosclerosis and psychiatric disorders. By critically evaluating the existing data, this study aims to provide insights into the potential interplay between these medical conditions. Data Sources: PubMed, Embase, Ebsco, Proquest, and Web of Science Review Methods: PubMed, Embase, Ebsco, Proquest, and Web of Science databases were queried for original English articles from 1950 to 2023. This review was conducted in accordance with the 2020 PRISMA guidelines. The publications were screened by two independent viewers. The Newcastle–Ottawa Scale quality instrument was used to assess the quality of studies. Results: Initially, 153 abstracts were screened for eligibility. After a rigorous selection process, five studies met the criteria, collectively encompassing 262 patients diagnosed with otosclerosis. The reported mean ages ranged from 25 to 52 years. A combined assessment of psychiatric disorder rates among otosclerosis patients revealed a rate of 36%. Depression, anxiety, and schizophrenia were the most common mental illnesses reported in all six studies. One of the studies specifically examined psychiatric disorder rates before and after stapedectomy, revealing a statistically significant decrease in depressive and anxiety-related symptoms following the surgical intervention. Conclusions: This systematic review emphasizes the emerging evidence connecting otosclerosis with psychiatric disorders and underscores the importance of adopting a multidisciplinary approach to assess and manage otosclerosis patients.
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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 | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".