DOES HEARING LOSS MODERATE THE RELATIONSHIP BETWEEN COGNITION AND DEPRESSION?
Bibliographic record
Abstract
Abstract Hearing loss, depression, and cognitive impairment are common, co-occurring conditions experienced by older adults, but the nature of the relationship among these conditions remains unclear. Both depression and hearing loss have been identified as risk factors for dementia, but for different stages of life (e.g., Singh-Manoux et al., 2017; Livingston et al., 2020). Hearing loss is described as the strongest modifiable risk factor in mid-life, and depression is second only to smoking as the strongest modifiable risk factor in late-life (Livingston et al., 2020). The association between hearing loss and depression is well documented in the literature (e.g., Lawrence et al., 2015). It is possible that hearing loss, as an earlier risk factor, moderates the relationship between depression and cognition. Therefore, as part of an ongoing longitudinal study of early indicators of cognitive decline, we examined the moderating effect of two measures of hearing (pure-tone hearing threshold average, Dichotic Sentence Identification test) on the relationship between depression (Geriatric Depression Scale) and cognition (Montreal Cognitive Assessment, Digit Symbol Substitution test) for a group of 65 older adults (mean age 74). Although none of the hearing or cognitive measures were significant predictors of depression scores on their own, there was a significant negative interaction between the Montreal Cognitive Assessment and pure-tone hearing threshold average, p = 0.048. These results may be interpreted to suggest that individuals with higher (poorer) hearing thresholds show a decrease in depression as their cognitive scores improve, whereas individuals with lower (better) hearing thresholds show the opposite pattern.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".