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Detailed analysis of the impact of age on the QT interval.

2016· article· en· W2546507504 on OpenAlexaff
Simon W. Rabkin, Xin-Bo Justin Cheng, Darby Thompson

Bibliographic record

VenuePubMed · 2016
Typearticle
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
Fundersnot available
KeywordsMedicineQT intervalFramingham Heart StudyNational Health and Nutrition Examination SurveyDemographyPopulationInternal medicineCardiologyFramingham Risk ScoreDiseaseEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVE: To analyze the effect of age on the ECG QT interval, an important predictor of cardiovascular mortality and drug-induced cardiac arrhythmias, and determine whether QT-heart rate correction formulae (QTc) have differential relationships with age and sex. METHODS: Data were examined from the US National Health and Nutrition Examination Survey (NHANES) II and III, civilian population aged 25 to 90 years. QT weighted means and standard deviations were calculated for all ages. The QTc were evaluated for six QTc: proposed by Bazett (QTcBZT), Fridericia (QTcFRD), Hodges (QTcHDG), Dmitrienko (QTcDMT), Rautaharju (QTcRTHa) and Framingham (QTcFRM). RESULTS: < 0.001) positive relationship between QTc variance and age for each QTc formula for both men and women. There were a greater proportion of individuals with longer QTc with older ages especially age 80 years and above. CONCLUSION: QTc and its variance increase with age. Prolonged QTc is more prevalent in older individuals, especially men.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.255
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 source (direct Gemma or distilled Codex), 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

Citations81
Published2016
Admission routes1
Has abstractyes

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