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Record W4387029461 · doi:10.31579/2578-8868/232

Medium Term Study of Incidence, Risk Factors and Type of Stroke in Hypertensive Patients

2022· article· en· W4387029461 on OpenAlexaff
Jiming Zou

Bibliographic record

VenueNeuroscience and Neurological Surgery · 2022
Typearticle
Languageen
FieldMedicine
TopicTraditional Chinese Medicine Studies
Canadian institutionsHealth Care Foundation
FundersStrong
KeywordsMedicineHyperlipidemiaIncidence (geometry)Internal medicineDiabetes mellitusBlood pressureStroke (engine)Cerebral infarctionType 2 diabetesLogistic regressionRisk factorGroup BBlood lipidsGastroenterologyCholesterolIschemiaEndocrinology

Abstract

fetched live from OpenAlex

Background and Aims: To investigate the incidence and risk factors of stroke in hypertension patients over 60 years of age, and to investigate the correlation. Methods: A retrospective analysis of 454 hypertension patients who were diagnosed through examination were divided into 4 groups, of which included group A (hypertension plus type 2 diabetes) were 4 cases, group B (hypertension plus hyperlipidemia) were 367 cases, group C (hypertension plus type 2 diabetes and hyperlipidemia) were 47 cases, and group D (hypertension) were 36cases. Every patient was given observation after definite diagnosis and treatment of 54 months, and to investigate the incidence of acute cerebral infarction, cerebral hemorrhage. Results: There were 44 patients occurred acute cerebral infarction in all 4 groups. Of these cases, there were 1 case in group A, 31 cases in group B,10 cases in group C and 2 cases in group D. In group B, 3 cases occurred cerebral hemorrhage. Logistic regression analysis showed systolic blood pressure (OR=1.106, P=0.001), fasting glucose (OR=2.059, P=0.000) and low-density lipoprotein cholesterol (LDL-C) (OR=0.104, P=0.025) were risk factors for stroke. Conclusions: Higher levels of blood glucose and/or blood lipid associate with higher risk of stroke in patients with hypertension.

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)0.002
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 score0.004
Threshold uncertainty score0.264

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.058
GPT teacher head0.279
Teacher spread0.221 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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