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Record W3042903746 · doi:10.3389/fneur.2020.00699

Post-stroke Cognitive Impairment—Impact of Follow-Up Time and Stroke Subtype on Severity and Cognitive Profile: The Nor-COAST Study

2020· article· en· W3042903746 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueFrontiers in Neurology · 2020
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
FundersFaculty of Medicine and Health, University of SydneySt. Olavs Hospital Universitetssykehuset i TrondheimHaukeland UniversitetssjukehusNasjonalforeningen for FolkehelsenNorges Teknisk-Naturvitenskapelige Universitet
KeywordsStroke (engine)MedicineMontreal Cognitive AssessmentCognitionPopulationLogistic regressionInternal medicinePhysical therapyCognitive impairmentPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Post-stroke cognitive impairment (PSCI) is common, but evidence of cognitive symptom profiles, disease course over time, and pathogenesis is scarce. We investigated whether time and etiologic stroke subtype were of importance for the probability for PSCI and severity and cognitive profile. METHODS: Stroke survivors (n=617) underwent cognitive assessments of attention, executive function, memory, language, perceptual-motor function and administered the Montreal Cognitive Assessment (MoCA) after 3 and/or 18 months. PSCI was classified according to Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria. Stroke severity was assessed with the National Institutes of Health Stroke Scale (NIHSS) at admittance and stroke subtype was categorized as intracerebral hemorrhage (ICH), large artery disease (LAD), cardioembolic stroke (CE), small vessel disease (SVD), un-/other determined strokes (UD). Mixed-effects logistic or linear regression was applied, with PSCI, MoCA, and z-scores of the cognitive domains as dependent variables. Independent variables were time as well as stroke subtype, time, and interaction between these. The analyses were adjusted for age, education, and sex. RESULTS: Mean age was 72 years (SD 12), 42 % were females, and mean NIHSS score at admittance was 3.8 (SD 4.8). Probability for PSCI after 3 and 18 months was 0.59 (95%CI 0.51-0.66) and 0.51 (95%CI 0.52-0.60) respectively and did not change over time. Global measures and almost all cognitive domains were impaired for the entire stroke population and for almost all stroke subtypes. Executive function and language improved for the entire stroke population, and after dividing the sample according to stroke subtypes, language improved for ICH patients. No significant differences were found in the severity of impairment between stroke subtypes, except for attention which was impaired for LAD and CE in contrast to no impairment for SVD. CONCLUSIONS: PSCI is common for all stroke subtypes, with impairment in several cognitive domains noted early after a stroke as well as a long time after a stroke. Increased evidence of symptom profile might be important for personalizing rehabilitation, while stroke subtypes may offer new insight into underlying mechanisms. Further research is needed on underlying mechanisms, prevention and treatment of PSCI, and on relevance for rehabilitation.

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.

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)0.000
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 score0.018
Threshold uncertainty score0.825

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.251
Teacher spread0.241 · 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