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Record W4205571109 · doi:10.32920/ryerson.14656017

Visual Magnocellular Deficits In Dyslexia : Are These Deficits Due To Co-morbidity With ADHD?

2021· preprint· en· W4205571109 on OpenAlexaff
Diane Lam

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsDyslexiaPsychologyDevelopmental dyslexiaSchizophrenia (object-oriented programming)Cognitive psychologyBiological theories of dyslexiaBackward maskingDevelopmental psychologyNeuroscienceAudiologyPsychiatryMedicineReading (process)Perception

Abstract

fetched live from OpenAlex

Some cases of dyslexia may be accounted for by a visual problem involving the magnocellular pathways. Research on dyslexia and problems in the magnocellular pathway has been controversial. Some studies indicate that individuals with dyslexia have problems in this pathway whereas other studies have not. It may be that only the individuals with both dyslexia and ADHD have problems in this pathway while individuals with dyslexia only are spared. In support of this, research has shown that individuals with schizophrenia have attention deficits (similar to those seen in individuals with ADHD) and problems in the magnocellular pathway. In the present study, controls, participants with dyslexia only, participants with both dyslexia and ADHD, and participants with ADHD only completed central and peripheral backward masking experiments. It was predicted that the two groups of participants with ADHD would have problems in the magnocellular pathway. Some evidence was found in support of this.

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)0.003
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 score0.008
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.063
GPT teacher head0.349
Teacher spread0.286 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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