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Record W1487119466 · doi:10.1002/lary.23837

Sudden sensorineural hearing loss is correlated with an increased risk of acute myocardial infarction: A population‐based cohort study

2013· article· en· W1487119466 on OpenAlexaff
Charlene Lin, Shih‐Wei Lin, Yung‐Song Lin, Shih‐Feng Weng, Tsung‐Ming Lee

Bibliographic record

VenueThe Laryngoscope · 2013
Typearticle
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineMyocardial infarctionHazard ratioIncidence (geometry)Confidence intervalCohortPopulationCohort studyPediatricsRetrospective cohort studyInternal medicineHearing lossAudiology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.250
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations49
Published2013
Admission routes1
Has abstractyes

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