Deterioro cognitivo en pacientes con hipoacusias comparados con pacientes con audición normal
Bibliographic record
Abstract
Background: Hearing loss affects 360 million people worldwide and it is linked to cognitive impairment, which is the main precursor of dementia. Hearing loss is the major modifiable risk factor for cognitive impairment. This study examines the relationship between severe/profound hearing loss and cognitive impairment in people aged 50 to 65 years. Objective: To determine whether severe/profound hearing loss accelerates the development of cognitive impairment in patients with this condition compared to those with normal hearing or mild hearing loss. Material and methods: Analytical cross-sectional study conducted in 254 patients. Degrees of hearing loss were assessed by tonal audiometry using the World Health Organization (WHO) criteria, and the degree of cognitive impairment with the Montreal Cognitive Assessment (MoCA) test. The variables analyzed included age, sex, schooling and socioeconomic level. Results: 82.8% of patients with severe/profound hearing loss had cognitive impairment, compared to 17.2% of those with normal hearing (p < 0.001), demonstrating a highly significant association between hearing loss and cognitive impairment. Conclusions: Patients with severe/profound hearing loss have greater cognitive impairment than those with normal hearing. Early diagnosis and treatment, such as the use of hearing aids, is key to prevent cognitive impairment and improve quality of life.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 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".