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Record W4307430104 · doi:10.12788/acp.0089

Anxiety and Depression in Stroke: An Evaluation of these Psychopathologies on Outcomes of Stroke Type using the National Inpatient Sample

2022· article· en· W4307430104 on OpenAlexaff
Obiora E. Onwuameze, Vineka Heeramun, Steven Scaife, Andrew T Olagunju, Malathi Pilla, Jude Ogugua, Daniel Boeder

Bibliographic record

VenueAnnals of Clinical Psychiatry · 2022
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsDepression (economics)AnxietyStroke (engine)MedicineNeurologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Anxiety and depression have been reported to complicate the course of stroke. This study evaluated the association of anxiety and depression independently on ischemic vs non-ischemic stroke. METHODS: A cross-sectional survey of 4,983,807 admissions for acute stroke from 1994 to 2013 in the National Inpatient Sample compared stroke patients with depression and anxiety to stroke patients with no psychiatric comorbidities. The database was operationalized based on the inclusion/exclusion criteria approved by the Southern Illinois University School of Medicine Institutional Review Board. RESULTS: Patients with anxiety and depression were more likely to have an ischemic stroke (OR 1.64; 95% CI, 1.61 to 1.68) vs a non-ischemic stroke (OR 1.25; 95% CI, 1.23 to 1.27). Inpatient mortality was significantly less in both the depression and anxiety groups compared to the control group. CONCLUSIONS: Psychiatric disorders (anxiety and depression) may increase the risk of ischemic stroke; however, depressed and anxiety patients with ischemic stroke were less likely to die from stroke. Further well-designed studies are necessary to explore these findings.

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)0.003
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 score0.010
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
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 head0.287
GPT teacher head0.522
Teacher spread0.235 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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