Oral language of school-aged children born pretermaturely: a population-based analysis from Madeira Island, Portugal
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".