Enhancing Clinical Visibility of Hearing Loss in Cognitive Decline
Bibliographic record
Abstract
BACKGROUND: Hearing loss is the largest potentially modifiable risk factor for dementia and is highly prevalent among older adults, yet it goes largely unreported, unidentified, and untreated, at great cost to health and quality of life. Hearing screening is a proven cost-effective solution to overcome delays in its identification and management yet is not typically recommended by physicians for older adults. OBJECTIVE: To demonstrate the feasibility and value of hearing screening for older adults at risk for dementia in order to enhance physicians' awareness of hearing loss and improve access to timely hearing care. METHODS: Patients referred to two academic medical clinics for memory disorders were offered hearing screening as part of clinic protocol. Patients with hearing loss were recruited to the study if they consented to a post-appointment telephone interview and chart review. Memory Clinic physicians were surveyed about the usefulness of the screening information and referral of patients with hearing loss to audiology. RESULTS: Hearing loss was reliably detected in Memory Clinic patients with both in-office and online screening tools. Physicians reported that screening enhanced their awareness of hearing loss and increased the referral rate to audiology. CONCLUSION: Hearing screening in Memory Clinic patients is a useful component of clinic protocol that facilitates timely access to management and addresses an important risk factor for dementia.
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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 | 0.003 | 0.018 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".