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Record W2096609541 · doi:10.1177/0022219413492853

Putative Risk Factors in Developmental Dyslexia

2013· article· en· W2096609541 on OpenAlexaff
Sara Mascheretti, Cecilia Marino, Daniela Simone, Ermanno Quadrelli, Valentina Riva, Maria Rosaria Cellino, Michel Maziade, Chiara Brombin, Marco Battaglia

Bibliographic record

VenueJournal of Learning Disabilities · 2013
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsUniversité LavalInstitut Universitaire en Santé Mentale de Québec
Fundersnot available
KeywordsDyslexiaPsychologyDevelopmental psychologyDevelopmental dyslexiaLearning disabilityReading (process)Linguistics

Abstract

fetched live from OpenAlex

Although dyslexia runs in families, several putative risk factors that cannot be immediately identified as genetic predict reading disability. Published studies analyzed one or a few risk factors at a time, with relatively inconsistent results. To assess the contribution of several putative risk factors to the development of dyslexia, we conducted a case-control study of 403 Italian children, 155 with dyslexia, by implementing a stepwise logistic regression applied to the entire sample, and then to boys and girls separately. Younger parental age at child's birth, lower parental education, and risk of miscarriage significantly increased the odds of belonging to the dyslexia group (19.5% of the variation). These associations were confirmed in the analyses conducted separately by sex, except for parental education, which significantly affected only males. These findings support reading disabilities as a multifactorial disorder and may bear some importance for the prevention and/or early detection of children at heightened risk for dyslexia.

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)0.007
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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.264
Teacher spread0.242 · 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
Published2013
Admission routes1
Has abstractyes

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