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Record W2054240980 · doi:10.1002/ddrr.48

Mathematical development in spina bifida

2009· review· en· W2054240980 on OpenAlexaff
Lianne H. English, Marcia A. Barnes, Heather B. Taylor, Susan H. Landry

Bibliographic record

VenueDevelopmental Disabilities Research Reviews · 2009
Typereview
Languageen
FieldMathematics
TopicCognitive and developmental aspects of mathematical skills
Canadian institutionsUniversity of Guelph
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsSpina bifidaDevelopmental psychologyPsychologyCognitionPopulationPediatricsMedicinePsychiatry

Abstract

fetched live from OpenAlex

Spina bifida (SB) is a neural tube defect diagnosed before or at birth that is associated with a high incidence of math disability often without co-occurring difficulties in reading. SB provides an interesting population within which to examine the development of mathematical abilities and disability across the lifespan and in relation to the deficits in visual-spatial processing that are also associated with the disorder. An overview of math and its cognitive correlates in preschoolers, school-age children and adults with SB is presented including the findings from a longitudinal study linking early executive functions in infancy to the development of later preschool and school age math skills. These findings are discussed in relation to socio-historical perspectives on math education and implications for intervention and directions for further research are presented.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.417
GPT teacher head0.504
Teacher spread0.087 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations29
Published2009
Admission routes1
Has abstractyes

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