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Record W1840024070 · doi:10.1111/ijs.12511

Cognitive Performance following Lacunar Stroke in Spanish-Speaking Patients: Results from the SPS3 Trial

2015· article· en· W1840024070 on OpenAlexafffund
Claudia Jacova, Lesly A. Pearce, Ana Roldan, Antonio Araúz, Jorge Tapia, Raymond M. Costello, Leslie A. McClure, Robert G. Hart, Oscar Benavente

Bibliographic record

VenueInternational Journal of Stroke · 2015
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsMcMaster University
FundersNational Institute of Neurological Disorders and StrokeNational Institutes of HealthAlzheimer Nadační FondAlzheimer Society
KeywordsMedicineNeuropsychologyCognitionCognitive impairmentLatin AmericansStroke (engine)Memory impairmentAudiologyCognitive skillPediatricsPsychiatryLinguistics

Abstract

fetched live from OpenAlex

BACKGROUND: Cognitive impairment is frequent in lacunar stroke patients. The prevalence and pattern among Spanish-speaking patients are unknown and have not been compared across regions or with English-speaking patients. AIMS: The aim of this study was to characterize cognitive impairment in Spanish-speaking patients and compare it with English-speaking patients. METHODS: The baseline neuropsychological test performance and the prevalence of mild cognitive impairment, defined as a z-score ≤ -1.5 on memory and/or non-memory tests, were evaluated in Spanish-speaking patients in the Secondary Prevention of Small Subcortical Strokes trial. RESULTS: Out of 3020 participants, 1177 were Spanish-speaking patients residing in Latin America (n = 693), the United States (n = 121), and Spain (n = 363). Low education (zero- to eight-years) was frequent in Spanish-speaking patients (49-57%). Latin American Spanish-speaking patients had frequent post-stroke upper extremity motor impairment (83%). Compared with English-speaking patients, all Spanish-speaking patient groups had smaller memory deficits and larger non-memory/motor deficits, with Latin American Spanish-speaking patients showing the largest deficits median z-score -1.3 to -0.6 non-memory tests; ≤5.0 for Grooved Pegboard; -0.7 to -0.3 for memory tests). The prevalence of mild cognitive impairment was high and comparable with English-speaking patients in the United States and Latin American Spanish-speaking patients but not the Spanish group: English-speaking patients = 47%, Latin American Spanish-speaking patients = 51%, US Spanish-speaking patients = 40%, Spanish Spanish-speaking patients = 29%, with >50% characterized as non-amnestic in Spanish-speaking patient groups. Older age [odds ratio per 10 years = 1.52, confidence interval = 1.35-1.71), lower education (odds ratio 0-4 years = 1.23, confidence interval = 0.90-1.67), being a Latin American resident (odds ratio = 1.31, confidence interval = 0.87-1.98), and post-stroke disability (odds ratio Barthel Index <95 = 1.89, confidence interval = 1.43-2.50) were independently associated with mild cognitive impairment. CONCLUSIONS: Mild cognitive impairment in Secondary Prevention of Small Subcortical Strokes Spanish-speaking patients with recent lacunar stroke is highly prevalent but has a different pattern to that observed in English-speaking patients. A combination of socio-demographics, stroke biology, and stroke care may account for these differences.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.302
Teacher spread0.266 · 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".

Quick stats

Citations8
Published2015
Admission routes2
Has abstractyes

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