MétaCan
Menu
Back to cohort
Record W2624311812 · doi:10.5539/jedp.v7n2p33

Cognitive Correlates of Japanese Language (Hiragana) Reading Abilities among School-Aged very Low Birth Weight Children

2017· article· en· W2624311812 on OpenAlexvenueno aff
Motohiro Isaki, Tadahiro Kanazawa, Toshihiko Hinobayashi, Hiroyuki Kitajima

Bibliographic record

VenueJournal of Educational and Developmental Psychology · 2017
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsPsychologyReading (process)CognitionDevelopmental psychologyLow birth weightDyslexiaTask (project management)Test (biology)Phonological awarenessAudiologyCognitive psychologyLinguisticsLiteracyMedicine

Abstract

fetched live from OpenAlex

Previous studies have examined that the reading abilities of Very Low Birth Weight (VLBW) children are poorer than those of Normal Birth Weight (NBW) children. However, little is known about the cognitive functions that have been used to explain the reading problems in VLBW children. This study investigated that the effects of attention function on reading abilities in VLBW children. 23 VLBW children (mean age 9.1 years old) and 23 NBW children (mean age 9.2 years old) completed a reading test (containing word reading and non-word reading tasks), attention tasks, a phonological task and a naming task. The group differences were significant for the non-word reading task and attention tasks. Moreover, there were significant correlations between scores on the reading test and those on attention tasks. Multiple stepwise regression analysis suggested the reading scores were influenced by attention. These results of the present study suggest that attentional dyslexia is a characteristic of reading among VLBW children.

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 score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.012
GPT teacher head0.304
Teacher spread0.292 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Educational and Developmental PsychologySame topicInfant Development and Preterm CareFrench-language works237,207