MétaCan
Menu
Back to cohort
Record W2748825602 · doi:10.1080/87565641.2017.1353093

Prevalence and Predictors of Learning and Psychological Diagnoses Following Pediatric Arterial Ischemic Stroke

2017· article· en· W2748825602 on OpenAlexafffund
Tricia S. Williams, Kyla P. McDonald, Samantha D. Roberts, Nomazulu Dlamini, Gabrielle deVeber, Robyn Westmacott

Bibliographic record

VenueDevelopmental Neuropsychology · 2017
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsHospital for Sick Children
FundersHospital for Sick ChildrenMedical Psychiatry AllianceCentre for Addiction and Mental Health
KeywordsMedical diagnosisPsychologyAttention deficit hyperactivity disorderIntellectual disabilityStroke (engine)Family historyPsychiatryLearning disabilityClinical psychologyRisk factorMedicineInternal medicinePathology

Abstract

fetched live from OpenAlex

This study examined the prevalence of learning and psychological diagnoses and associated neurological and personal-environmental risk factors following perinatal and childhood arterial ischemic stroke. In our sample of 126 children and youth, 52.4% received a diagnosis following their assessment. Specifically, 32% had a single diagnosis and 21% had two or more diagnoses. Learning disability, attention deficit-hyperactivity disorder, and intellectual disability were the most prevalent diagnoses. Associated risk factors varied by diagnosis with lower intellectual functioning being the common risk factor across categories. Seizure status was associated with intellectual disability whereas family history was related to ADHD and comorbid diagnoses.

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)0.000
metaresearch head score (Gemma)0.004
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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.336
Teacher spread0.300 · 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

Citations41
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueDevelopmental NeuropsychologySame topicAttention Deficit Hyperactivity DisorderFrench-language works237,207