Sudden sensorineural hearing loss is correlated with an increased risk of acute myocardial infarction: A population‐based cohort study
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Previous studies have indicated that hypercholesterolemia and a high burden of cardiovascular risk factors are associated with the development of sudden sensorineural hearing loss (SSHL). The purpose of this study was to test the hypothesis that SSHL is a risk factor for the development of myocardial infarction (MI). STUDY DESIGN: A retrospective cohort study. METHODS: Using the Taiwan Longitudinal Health Insurance Database, we compared patients diagnosed with SSHL between January 1, 2001, and December 31, 2006, (N = 44,830) with age-matched controls (1:1) (N = 44,830). We followed up on each patient until the end of 2009 to evaluate the incidence of MI for a minimum period of 3 years after their initial SSHL diagnosis. RESULTS: We found that after adjusting for potential confounds with an adjusted hazard ratio (HR) of 1.254 (95% confidence interval, 1.092-1.440, P < 0.05), patients with SSHL were more likely to suffer MI than the control population. When stratified by patient age, the incidence of MI was 1.62-fold and 1.28-fold higher for SSHL-diagnosed patients aged between 50 and 64 years and those aged ≥ 65 years (P = 0.0064 and P = 0.0001), respectively, than in the non-SSHL group. CONCLUSIONS: SSHL may confer an independent risk of MI. This observation may prompt the early detection and timely treatment of patients at a high risk of MI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".