Elderly Individuals Residing in Nursing Homes in Western Romania Who Have Been Diagnosed with Hearing Loss are at a Higher Risk of Experiencing Cognitive Impairment
Bibliographic record
Abstract
Purpose: The objective of this research was to determine if there is any correlation between the severity of neurocognitive disorder and hearing impairment in the elderly. Patients and Methods: This is a population-based observational study that included subjects aged ≥ 65 years. They were evaluated for the existence of cardiovascular risk factors, diabetes, stroke, alcohol abuse, and smoking. Hearing impairment was diagnosed by an audiologist, using behavioral audiometric examination. These evaluations might have been performed in response to concerns about hearing loss, or they could have been a routine component of yearly comprehensive health screenings that included a Mini-Mental State Examination 2nd Edition (MMSE-2) test. According to the results of the MMSE-2 scale, we divided the individuals into two groups, Group I for those who had cognitive impairment and severe neurocognitive disorder, and Group II for those who did not have cognitive impairment. Results: The study enrolled 203 patients with a mean age of 77 ± 7.5 years (range 65-98), 99 (48%) were males. When comparing the two groups, group I patients presented more often cardiovascular risk factors, stroke, diabetes, and impaired hearing. The univariable logistic regression found that cognitive impairment was significantly more frequent in the elderly with cardiovascular disease, diabetes, and stroke (p<0.0001). The multivariate regression analysis found that stroke (p<0.0001) diabetes (p=0.0008), cardiovascular disease (p=0.0004), and impaired hearing (p=0.0011) were significantly linked to cognitive impairment. The occurrence of hearing impairment in the elderly was related to having an MMSE-2 score of 14 or below. Conclusion: According to the findings of this research, the elderly who have trouble hearing in addition to other conditions might have an increased risk for severe neurocognitive disorder.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".