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Record W3094544992 · doi:10.1101/2020.10.16.20214148

Vascular Risk Factor Prevalence and Trends in Native Americans With Ischemic Stroke - A National Inpatient Sample Analysis

2020· preprint· en· W3094544992 on OpenAlexaff
Dinesh Jillella, Sara Crawford, Rocío López, Atif Zafar, Anne Tang, Ken Uchino

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of Toronto
FundersCleveland Clinic
KeywordsMedicineEthnic groupDemographyStroke (engine)Incidence (geometry)Pacific islandersDiabetes mellitusIschemic strokeRisk factorGerontologyInternal medicineEnvironmental healthPopulationIschemia

Abstract

fetched live from OpenAlex

Abstract Introduction Native Americans have a higher incidence and prevalence of stroke and the highest stroke-related mortality among race-ethnic groups in the United States. We aimed to analyze trends in the prevalence of vascular risk factors among Native Americans with ischemic stroke over the last two decades along with a comparison to the other race-ethnic groups. Methods National/Nationwide Inpatient Sample (NIS) database was used to explore the prevalence of risk factors among hospitalized ischemic stroke patients during 2000 - 2016. Ischemic stroke and risk factors of interest were identified using validated ICD-9/10 codes. The race-ethnic groups of interest were Native American, White, Black, Hispanic, Asian/Pacific Islanders, and others. Crude and age-and sex-standardized prevalence estimates were calculated for each risk factor within each race-ethnic group in 6 time periods: 2000-02, 2003-05, 2006-08, 2009-11, 2012-14, and 2015-16. We explored linear trends over the defined time periods using linear regression models, with differences in trends between the Native American group and each of the other race-ethnic groups assessed using interaction terms. The analysis accounted for the complex sampling design, including hospital clusters, NIS stratum, and trend weights for analyzing multiple years of NIS data. Results Of the 1,278,784 ischemic stroke patients that were included in the analysis, Native Americans constituted 5472. The age-and-sex-standardized prevalence of hypertension (trend slope = 2.24, p < 0.001), hyperlipidemia (trend slope = 6.29, p < 0.001), diabetes (trend slope = 2.04, p = 0.005), atrial fibrillation/flutter (trend slope = 0.80, p = 0.011), heart failure (trend slope = 0.73, p = 0.036) smoking (trend slope = 3.65, p < 0.001), and alcohol (slope = 0.60, p = 0.019) increased during these time periods among Native Americans, while coronary artery disease prevalence remained unchanged. Similar upward trends of several risk factors were noted across other race-ethnic groups with Native Americans showing larger increases in hypertension prevalence compared to Blacks, Hispanics, and Asian/Pacific Islanders and in smoking prevalence compared to Hispanics and Asian/Pacific Islanders. By the year 2015-2016, Native Americans had the highest overall prevalence of diabetes, coronary artery disease, smoking, and alcohol among all the race-ethnic groups. Conclusion The prevalence of most vascular risk factors among ischemic stroke patients has increased in Native Americans and other race-ethnic groups over the last two decades. Significantly larger increases in the prevalence of hypertension and smoking were seen in Native Americans compared to other groups along with them having the highest prevalence in multiple risk factors in recent years.

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)0.002
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 score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.284
Teacher spread0.259 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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