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Record W4205657718 · doi:10.5209/rlog.74670

Oral language of school-aged children born pretermaturely: a population-based analysis from Madeira Island, Portugal

2022· article· en· W4205657718 on OpenAlexaff
A Marques, Maria Santos

Bibliographic record

VenueRevista de Investigación en Logopedia · 2022
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsCegep de Sept Iles
Fundersnot available
KeywordsGestational ageDevelopmental psychologyPsychosocialPopulationBirth weightLow birth weightLanguage developmentPediatricsPsychologyLanguage acquisitionPhonologyMedicinePregnancyLinguisticsPsychiatry

Abstract

fetched live from OpenAlex

Premature birth and low birth weight are very important factors in neurodevelopment. Current research in this population focuses on children born prematurely, with no underlying complications in the post-natal period, who are likely to develop specific disorders with their language development and consequently with their learning capabilities too. This study aims to analyse the oral language skills of prematurely born children in comparison to their school-aged peers. The children were assessed in the respective schools, 27 preterm children (16 under 32 weeks and 11 with 32 or more weeks of gestation) and 49 term paired by gender, age, and school year. Tests including simple and complex structures for assessing semantics, morphosyntax, and phonology were used, as well as a test of verbal memory. Preterm born children, regardless of their prematurity grade, showed significantly lower results than their peers, and more than a half of them, 52%, presented low scores in all language tests simultaneously, showing an important language deficit. In contrast, in the term born children group only 14% showed low scores simultaneously in all tests. Verbal memory ability proved to be lower than that of their term peers, regardless of the gestational age and birth weight of preterm children. As a result of this analysis we consider that the evaluation of the linguistic development of these children, even in cases of moderate to late prematurity, should be monitored in order to identify earlier the existence of deficits and prevent psychosocial and learning problems.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.255
Teacher spread0.245 · 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 teacher head, not a consensus.

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
Published2022
Admission routes1
Has abstractyes

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