Association between self-reported dizziness and asymmetric hearing loss in the older adults
Bibliographic record
Abstract
ABSTRACT Purpose: to verify the association between self-reported dizziness, degree and symmetry of hearing loss, age and gender in a sample of older adults. Methods: this retrospective study included the analysis of 440 records of older adults with a mean age of 72.9 years, enrolled from 2011 to 2015 in an auditory rehabilitation service. Binary logistic regression models were performed between the variables, and the data was analyzed using the SPSS 24.00 software. For all tests, alpha values were considered significant when lower than 0.05. Results: in the sample, 78 (17.7%) older adults had asymmetric hearing loss, and 27 (34.6%) of them complained of dizziness. Self-reported complaint of dizziness was significantly associated with female gender (p<0,001), to severe hearing loss (p<0,001), age under 70 years, and with asymmetric hearing loss(p<0,001). Conclusion: in this study, younger female elderlies with severe asymmetric hearing loss presented self-reported complaint of dizziness . These results suggest that this population should be routinely screened for balance problems in order to provide rehabilitation programs to avoid future falls.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".