Practices and perceptions of cognitive assessment for adults with age‐related hearing loss
Bibliographic record
Abstract
OBJECTIVES: To investigate the landscape of cognitive impairment (CI) screening for adults with age-related hearing loss (ARHL) among otolaryngologists and audiologists. To identify provider factors and patient characteristics that impact rates of CI screening and referral. METHODS: A 15 question online survey was sent to members of the Georgia Society of Otolaryngology (GSO), Georgia Academy of Audiology (GAA), American Otological Society and American Neurotology Society (AOS/ANS), and posted on the web forum for two hearing disorders special interest groups within the American-Speech-Language-Hearing Association (ASHA). Responses were collected anonymously. Chi-square tests were used to compare responses. RESULTS: < .001). The complaint of a neurological symptom, such as memory loss, would prompt screening or referral for only 27.3% (n = 18) and 51.52% (n = 34) of respondents, respectively. Forty-two percent (n = 28) of respondents suggested CI screening with the MMSE vs 20% (n = 13) with the Montreal Cognitive Assessment. CONCLUSIONS: Despite recommendations for cognitive assessment in high-risk populations, such as older adults with ARHL, the practice of CI screening and referral is not yet commonplace among otolaryngologists and audiologists. These providers have a unique opportunity to assess adults with ARHL for CI and ensure appropriate referral. LEVEL OF EVIDENCE: 5.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".