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Abstract 2189: Pre-existing Dementia, Death And Disability After Ischemic Stroke: Results From A Propensity-score Matched Analysis

2012· article· en· W123425387 on OpenAlexaffabout
Gustavo Saposnik, Moira K. Kapral, Robert Côté, Paula A. Rochon, Stavroula Raptis, Julie Wang, Muhammad Mamdani, Sandra Black

Bibliographic record

VenueStroke · 2012
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsSunnybrook Health Science CentreInstitute for Clinical Evaluative SciencesMcGill UniversityUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineDementiaStroke (engine)ThrombolysisModified Rankin ScaleIntracerebral hemorrhagePropensity score matchingPopulationEmergency medicinePhysical therapyInternal medicineIschemic strokeMyocardial infarctionDiseaseSubarachnoid hemorrhageIschemia

Abstract

fetched live from OpenAlex

Background: With an aging population, patients are increasingly likely to present with stroke and pre-existing dementia, which may lead to greater death and disability. Objective: To assess the risk of all-cause mortality and poor functional outcomes after ischemic stroke in patients with and without pre-existing dementia. Design, Participants, and Setting: We conducted a multicenter cohort study of all patients presenting to 12 tertiary care institutions participating in the Registry of the Canadian Stroke Network (RCSN) with a first ischemic stroke between 2003 and 2008. Individuals with pre-existing dementia were matched using propensity score methods with patients without dementia during their index hospitalization based on these characteristics: age (within 3 years), sex, stroke severity, stroke subtype (lacunar vs. non-lacunar), level of consciousness, vascular risk factors, dysphagia, glucose and creatinine on admission, Charlson index, residence prior to hospitalization (home vs. other), pre-admission dependency, hospital arrival via ambulance, admission to stroke unit, thrombolysis, and palliative care. A propensity score model for dementia was estimated that balanced prognostically-important baseline covariates between subjects with and without dementia. Data Sources: Registry of the Canadian Stroke Network (RCSN) and Registered Persons Database (RPDB) Main Outcome Measures: The primary outcome was all cause mortality at 30-days. Secondary outcomes included mortality at discharge and at 1 year, disability at discharge (modified Rankin scale ≥3), medical complications (pneumonia), and discharge disposition. A subgroup analysis assessing the risk of intracerebral hemorrhage among those receiving thrombolysis was also conducted. Results: We matched 877 patients with an acute ischemic stroke and pre-existing dementia to 877 stroke patients without dementia. Patients were well-matched. The mean age was 82 years and 58% were women. Mortality at discharge, 30 days and one year after stroke was similar in patients with and without dementia [for mortality at discharge: RR 0.88 (95% confidence interval (CI) 0.74 to 1.05); mortality at 30-days: RR 0.88 (95%CI 0.75 to 1.03) and mortality at 1 year: RR 1.01; 95%CI 0.92 to 1.11). Patients with pre-existing dementia had similar disability at discharge and home disposition. In the subgroup of patients who received thrombolysis, there were no differences between those with and without dementia in the risk intracerebral hemorrhage (RR 1.27; 95%CI 0.69- 2.35) and no differences in mortality or disability at discharge. Conclusions: Pre-existing dementia is not independently associated with mortality, disability, or institutionalization after ischemic stroke. Pre-existing dementia should not necessarily preclude access to thrombolytic therapy and specialized stroke care.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.036
GPT teacher head0.283
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2012
Admission routes2
Has abstractyes

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