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Record W2477259888 · doi:10.1080/09297049.2016.1205008

Working memory outcomes following unilateral arterial ischemic stroke in childhood

2016· article· en· W2477259888 on OpenAlexaff
Amanda Fuentes, Robyn Westmacott, Angela Deotto, Gabrielle deVeber, Mary Desrocher

Bibliographic record

VenueChild Neuropsychology · 2016
Typearticle
Languageen
FieldPsychology
TopicCognitive Abilities and Testing
Canadian institutionsYork UniversityHospital for Sick Children
Fundersnot available
KeywordsWorking memoryStroke (engine)PsychologyPediatric strokeIschemic strokePopulationArterial Ischemic StrokeMedicineCognitionPsychiatryIschemia

Abstract

fetched live from OpenAlex

There is a dearth of research examining working memory (WM) following pediatric arterial ischemic stroke (AIS). This study assesses the WM patterns of 32 children, aged 6 to 14 years, with a history of unilateral AIS and 32 controls using a paradigm based on Baddeley and Hitch's multi-component WM model. The results indicate compromised WM in children with AIS relative to controls and parent reports confirm higher rates of dysfunction. Supplementary analyses of impairment confirm higher rates in children with AIS, ranging from 31.25% to 38.70% on performance-based measures and 50.00% on parent reports, compared to 0.00% to 21.88% on performance-based measures in controls and 15.63% on parent reports. Continual follow-up is recommended given that a subset of children with stroke appear to be at risk for WM impairment. Moreover, the subtle nature of WM challenges experienced by many children who have experienced a stroke increases the likelihood that WM impairment could go undetected. The long-term trajectories of WM in the pediatric stroke population remains unknown and future studies are needed to track changes in WM functioning over time.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.284
Teacher spread0.262 · 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

Citations18
Published2016
Admission routes1
Has abstractyes

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