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Record W2787230585 · doi:10.9741/23736658.1076

Predicting risk factors of working aged hemorrhagic stroke patients in a tertiary teaching hospital in Chiang Mai

2018· article· en· W2787230585 on OpenAlexvenueno aff
Suphannee Triamvisit, Wilaiwan Chongruksut, Wanarak Watcharasaksilp, Rujee Rattanasathien

Bibliographic record

VenueAsian/Pacific Island Nursing Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
FundersFaculty of Medicine, Chiang Mai UniversityChiang Mai University
KeywordsChiang maiStroke (engine)EpidemiologyMedicineTeaching hospitalEmergency medicineFamily medicineInternal medicineSocioeconomics

Abstract

fetched live from OpenAlex

Stroke is the third leading cause of morbidity and mortality in Thailand accounting for a significant and increasing share of hospital costs. The purpose of this project is to study the epidemiology of the prevalence and its predicting factors of working aged hemorrhagic stroke (HS) patients admitted at a tertiary teaching hospital in Chiang Mai, Thailand. We conducted a five-year retrospective descriptive study. The subjects in this study were patients diagnosed with HS, between 15-59 years of age, and admitted to a tertiary teaching hospital in Chiang Mai, Thailand from January 2009 to December 2013. A total of 404 working aged adults who had HS were admitted to the hospital during this review period; 60.9% males and 39.1% females. Nearly 70% of patients were between 46-59 years of age (M = 47.5, SD = 9.8). Of the patients admitted to the hospital, 76.7% were transferred there from other hospitals. Intracerebral hemorrhage was present in 59.7% of patients. Severe HS occurred in 35.9% of the patients with a Glasgow Coma Score from 3-8. Approximately 69% of the working aged HS patients required surgery. The top five identified risk factors for HS were hypertension (83.4%), hyperlipidemia (38.9%), alcohol consumption (21.5%), smoking (15.3 %), and drug non-adherence (14.9%). We found significantly associated risk factors in working-aged HS by multivariate analysis among male gender (p < .001), drug non-adherence (p = .047), and hypertension (p = .048). Raising awareness to reduce risk behavior and health promotion in the community are the keynotes for health care providers in working-aged HS prevention.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.236
Teacher spread0.228 · 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.

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

Citations1
Published2018
Admission routes1
Has abstractyes

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