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Record W2151329285 · doi:10.1177/00222194060390020601

Arithmetic Processing in Children With Spina Bifida

2006· article· en· W2151329285 on OpenAlexaff
Marcia A. Barnes, Margaret Wilkinson, Ekta Khemani, Amy Boudesquie, Maureen Dennis, Jack Μ. Fletcher

Bibliographic record

VenueJournal of Learning Disabilities · 2006
Typearticle
Languageen
FieldMathematics
TopicCognitive and developmental aspects of mathematical skills
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of GuelphUniversity of Toronto
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsSubtractionPsychologyLearning disabilitySpina bifidaReading (process)CognitionNeuropsychologyArithmeticDyscalculiaDevelopmental psychologyAudiologyDyslexiaMathematicsPsychiatryPediatricsMedicine

Abstract

fetched live from OpenAlex

Three studies compared 98 children with spina bifida myelomeningocele (SBM)-a disorder associated with high rates of math disability and spatial deficits-to 94 typically developing children on multidigit subtraction and cognitive addition tasks. Children with SBM were classified into those with reading decoding and math disability, only math disability, and no reading or math disability. Study 1 showed that visual-spatial errors in multidigit arithmetic were not elevated in children with SBM. In Study 2, deficits in accuracy, speed, and strategy-use in single-digit addition characterized groups with math disability regardless of reading status. Accuracy and speed on single-digit addition was strongly related to performance on multidigit subtraction. A math-level matching design in Study 3 revealed less mastery of math facts by the group with SBM. The results are discussed with reference to cognitive and neuropsychological models of math disability.

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)0.005
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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
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 head0.015
GPT teacher head0.272
Teacher spread0.257 · 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

Citations74
Published2006
Admission routes1
Has abstractyes

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