Bibliographic record
Abstract
Hearing is an important sensation to the elderly as it promotes their quality of life and maintains their safety and wellness. For example, healthy hearing lets the elderly catch alarm sounds, stay alert to danger whilst asleep, listen in the dark, detect sounds from behind, communicate efficiently with other people, and maintain links to the world via telephone and radio, especially after retirement. However, age-related hearing loss, i.e., presbycusis, seems to become a growing problem in our community. The purpose of this study is to demonstrate whether presbycusis is a critical issue in our community. To achieve this purpose, the data in the literature as well as in the websites sponsored by hearing-related professional associations and sponsored by related government’s departments have been searched and reviewed. The data resulted from the review show a high prevalence of presbycusis, an ever-growing senior population, an incredible increase in hearing impairment and presbycusis population in the next two decades, a rank as high as at the third place for the prevalence of presbycusis among chronic health conditions in elderly resident facilities, and an alarmingly negative effect of presbycusis on mental health, social life, speech perception and hearing-related areas in the brain. These findings demonstrate that hearing loss in the elderly is a critical issue in our community. The etiology, clinical significance, management of presbycusis, prevention, and access of presbycusis population to assistive devices are also overviewed and discussed.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".