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Record W3198416310 · doi:10.19166/med.v9i1.4191

Association Between Cigarette Smoking And Cognitive Function In Stroke Patients Of Siloam Lippo Karawaci Hospital

2021· article· en· W3198416310 on OpenAlexaboutno aff
Raissa Putri Raspati, Pricilla Yani Gunawan

Bibliographic record

VenueMedicinus · 2021
Typearticle
Languageen
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMontreal Cognitive AssessmentCognitionStroke (engine)Cigarette smokingCognitive impairmentAssociation (psychology)Physical therapyInternal medicinePsychologyPsychiatry

Abstract

fetched live from OpenAlex

<div class="WordSection1"><p>Stroke is cerebrovascular disease, causing deterioration of brain function as a result of cerebral blood flow disruption. Stroke is the third leading cause of death in the world and is considered an important cause of long-term disability and cognitive impairment. Risk factors of stroke are further divided into unmodifiable risk factors and modifiable risk factors, with one of the most common modifiable risk factors of stroke, is cigarette smoking. Besides being one of the risk factors that cause stroke, cigarette smoking is believed to have a role in cognitive impairment. This study aims to obtain information regarding the association between cigarette smoking and cognitive function in stroke patients of Siloam Lippo Karawaci Hospital. This research is an unpaired comparative analytical study with a cross-sectional design. Data sampling was taken by consecutive sampling on 56 stroke patients of Siloam Lippo Karawaci Hospital. Cognitive function was made based on the Montreal Cognitive Assessment version Indonesia (MoCA-INA). All data were analyzed by Chi-Square test using SPSS version 25 and the result is considered significant if the p-value < 0,05. From the result of this study. There is a significant association between cigarette smoking and cognitive function (p-value 0,004 and OR 5,343).</p></div>

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 score0.040
Threshold uncertainty score0.337

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 head0.010
GPT teacher head0.260
Teacher spread0.250 · 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
Published2021
Admission routes1
Has abstractyes

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