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Record W4383067056 · doi:10.24018/ejmed.2023.5.4.1753

Factors Related to Early Versus Late Hospital Arrival in Acute Ischemic Stroke

2023· article· en· W4383067056 on OpenAlexaff
Mohammad Mojtahed, Sara Esmaeili, Sepideh Allahdadian, Samira Chaibakhsh, Ali Mojtahed, Samaneh Tanhapour Khotbehsara, Sina Eskandari Delfan, Mahya Naderkhani, Zahra Mirzaasgari

Bibliographic record

VenueEuropean Journal of Medical and Health Sciences · 2023
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineArrival timeStroke (engine)ThrombolysisAtrial fibrillationOdds ratioDiabetes mellitusBlood pressureInternal medicineCardiologyIschemic strokeRisk factorMyocardial infarctionIschemia

Abstract

fetched live from OpenAlex

Background: The onset-to-arrival time affects the decision of intravenous thrombolysis therapy which is associated with stroke’s prognosis. This study aimed to evaluate factor associated with early and late arrival of patients with Acute Ischemic Stroke and the effect on patients’ outcomes. Materials and Methods: Patients confirmed with acute ischemic stroke in central stroke centers were included in a prospective study. The patients were grouped into early arrivers (less than 4.5 hours) and late arrivers (at and after 4.5 hours). Patients’ data were obtained from the stroke registry system. Results: In Summary, Higher initial NIHSS, less than 15 km from the hospital, a history of CVA, Diabetes, Abnormal blood pressure, EMS transportation, Atrial fibrillation, and current use of anticoagulants, Not using opium, and not smoking was significantly associated with early arrival time. Normal blood pressure AF was a negative and significant (p-value=0.001, Odds ratio: 0.38) predictor of the late arrival. Normal blood pressure was a positive and significant (p-value<0.001, Odds ratio: 4.762) predictor of the late arrival. Conclusion: Some baseline factors are associated with the onset-arrival time of patients with ischemic stroke.

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)0.004
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 score0.482
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.053
GPT teacher head0.347
Teacher spread0.294 · 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
Published2023
Admission routes1
Has abstractyes

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