Association between audiometric patterns and probabilities of cardiovascular diseases
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to analyze the progression of the audiometric pattern of serial screening tests in companies with hearing conservation program (HCP) to clinical audiometric tests to identify individuals more susceptible to develop cardiovascular diseases (CVDs). The procedure is based on the analysis of various audiometric patterns that have been demonstrated to have a statistically significant relation to certain CVDs. Identifying these individuals, based on pattern progression of hearing loss, could result in earlier detection to prevent disease or decrease its morbidity. STUDY DESIGN: values were used to calculate the confidence intervals. METHODS: The analysis was based on potential risk factors related to CVD in 29 cohorts of 10,105 subjects. Of these, a total of 704 subjects also had clinical audiometric tests and examination by an ENT to verify the exactitude of the screening test questionnaire and pattern relation with the clinical audiogram. RESULTS: A first analysis was made on 704 subjects who had clinical evaluation and clinical audiometric tests showed results comparable to those of Friedland. A correlation between the questionnaire of the clinical and the self-reporting screening tests questionnaires was performed and showed a correlation between the following risk factors: diabetes, hypertension, hyperlipidemia and smoking. Analysis of the progression of audiometric patterns suggested a relationship with the predictive probabilities of developing CVDs. CONCLUSION: Progression toward low-frequency hearing loss patterns provides early identification of patients whose audiometric pattern progression suggests increased probability of developing CVDs. The treating physician, by prescribing further investigations, could potentially prevent or reduce the morbidity of these diseases. LEVEL OF EVIDENCE: III.
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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 | 0.005 |
| 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) | 0.003 | 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".