Subjective and objective hearing loss among US adult cancer survivors: A national cross-sectional study.
Bibliographic record
Abstract
e24059 Background: Approximately 13% of adults had some degree of hearing loss (HL), and the prevalence is higher among adults aged 65 and over (30.9%). Approximately 50-80% of the childhood, adolescent and young adult cancer survivor population report HL following treatment with high-dose platinum-based chemotherapy, head or brain radiotherapy. We aimed to 1) estimate the prevalence of subjective and objective hearing loss (HL) among cancer survivors and compare to the general population; 2) assess the performance of subjective HL questions in detecting HL by audiometry among cancer survivors. Methods: Adults aged 20-80 years old were selected from the National Health and Nutrition Examination Survey. The weighted prevalence of subjective HL (having troublesome hearing and tinnitus) and objective HL (speech-frequency HL and high-frequency HL) by audiometry test was calculated. Chi-square test and multi-adjusted logistic regression models were used to compare HL between cancer survivors and the general population. To evaluate the performance of subjective HL questions as a tool to screen for objective HL by audiometry, area under the curve (AUC) were estimated using age-and gender- adjusted logistic regression. Results: Compared to the general population, cancer survivors had a statistically significantly higher prevalence of troublesome hearing (aOR = 1.43; 95%CI:1.22-1.68), tinnitus (aOR = 1.28; 95%CI:1.08-1.53), speech-frequency HL (SFHL) (aOR = 1.44; 95%CI:1.20-1.73) and high-frequency HL (HFHL) (aOR = 1.74; 95%CI:1.45-2.09). The sensitivity and specificity of the combined subjective questions “whether having troublesome hearing and/or tinnitus” in detecting HL was shown in Table. The age- and gender- adjusted AUC for detecting SFHL and HFHL using combined questions was 0.88 and 0.90. Conclusions: Cancer survivors have a significantly higher prevalence of both subjective and objective HL, but a lack of hearing examination. Two subjective HL questions could accurately identify those have true HL and provide a simple and efficient screening tool for providers. Cancer survivors and their families should be educated and encouraged to discuss hearing concerns, and providers should facilitate raising awareness and provide early screening and timely referral when HL is identified.[Table: see text]
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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 | 0.001 | 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 | 0.000 | 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".