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Record W2059426093 · doi:10.1037/0894-4105.19.4.456

Space-Based Inhibition of Return in Children With Spina Bifida.

2005· article· en· W2059426093 on OpenAlexaff
Maureen Dennis, Kim Edelstein, Kim Copeland, Jon Frederick, David J. Francis, Ross Hetherington, Susan Blaser, Larry A. Kramer, James M. Drake, Michael E. Brandt, Jack Μ. Fletcher

Bibliographic record

VenueNeuropsychology · 2005
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsSickKids FoundationHospital for Sick Children
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsPsychologyInhibition of returnCued speechSpina bifidaMidbrainCovertAudiologyDevelopmental psychologyNeuroscienceCognitionCognitive psychologyCentral nervous systemMedicineVisual attentionPediatrics

Abstract

fetched live from OpenAlex

Inhibition of return (IOR) refers to an increase in time to react to a target in a previously attended location. Children with spina bifida meningomyelocele (SBM) and hydrocephalus have congenital dysmorphology of the midbrain, a brain region associated with the control of covert orienting in general and with IOR in particular. The authors studied exogenously cued covert orienting in 8- to 19-year-old children and adolescents (84 with SBM and 37 age-matched, typically developing controls). The exogenous cue was a luminance change in a peripheral box that was 50% valid for the upcoming target location. Compared with controls, children with SBM showed attenuated IOR in the vertical plane, a deficit that was associated with midbrain dysmorphology in the form of tectal beaking but not with posterior brain volume loss. The data add to the emerging evidence for SBM deficits in attentional orienting to salient information.

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)0.002
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 score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.008
GPT teacher head0.225
Teacher spread0.217 · 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

Citations33
Published2005
Admission routes1
Has abstractyes

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