Risk of Sudden Sensorineural Hearing Loss in Patients with Common Preexisting Sensorineural Hearing Impairment: A Population-Based Study in Taiwan
Bibliographic record
Abstract
OBJECTIVE: The role of preexisting sensorineural hearing impairment on the risk for sudden sensorineural hearing loss (SSHL) is still unclear. In this study, we aimed to assess the risk of SSHL in patients with common preexisting sensorineural hearing impairment using population-based data. METHODS: A population-based case-control study design was used to analyze claims data between January 2001 and December 2011 obtained from the Taiwan National Health Insurance Research Database. The cases consisted of 514 patients with SSHL and the controls were frequency matched to 2,570 cases by sex, 10-year age group, and year of index date. Common sensorineural hearing impairments were retrospectively assessed in the cases and controls. Associations between sensorineural hearing impairment and risk of SSHL were evaluated using unconditional univariate and multivariate logistic regression analyses. RESULTS: The mean age for the 3,084 study subjects was 53.1 years (standard deviation, S.D. = 15.6). Of the 514 cases, 49 (9.5%) had sensorineural hearing impairment while only 44 (1.7%) of the 2,570 controls had the same condition. Univariate logistic regression analyses indicated that preexisting sensorineural hearing impairment was significantly associated with SSHL (odds ratio, OR = 6.05, p < 0.001). Other comorbidities including hypertension, diabetes mellitus, and hyperlipidemia also showed significant associations with SSHL. Similar results were obtained when the association between SSHL and sensorineural hearing impairment was adjusted with either all the covariates (adjusted OR = 6.22, p < 0.001) or with only those selected using a backward elimination procedure (adjusted OR = 6.20, p < 0.001). CONCLUSIONS: Results from this population-based case-control study revealed that common sensorineural hearing impairment might be a novel risk factor for SSHL.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".