Evidence for Catch-up in Cognition and Receptive Vocabulary Among Adolescents Born Very Preterm
Bibliographic record
Abstract
BACKGROUND: Very preterm adolescents display persistent deficits in neuropsychological functions. OBJECTIVE: To compare cognitive and language outcomes at 16 years and cognitive and receptive vocabulary trajectories throughout school years between very preterm and term children and to determine child and family factors associated with better developmental trajectories. DESIGN AND METHODS: At 8, 12, and 16 years, 322 very preterm children with birth weights of 1250 g or less and 41 term children had cognitive and language testing. Hierarchical growth-curve modeling was used to delineate the differences in cognitive and receptive vocabulary development between participants. Cluster analyses allowed for the characterization of very preterm children with different patterns of cognitive and receptive vocabulary development. RESULTS: At 16 years, very preterm adolescents had deficits in general cognition and higher-order language skills (phonological awareness and phonemic decoding) compared with term peers. Although the between-group difference in cognitive scores remained stable from 8 to 16 years, very preterm children demonstrated catch-up gains in receptive vocabulary during the same period. Moreover, subgroups of very preterm children displayed developmental trajectories in cognition similar to term children (55% on the vocabulary and 46% on the block-design subtests). These children had lower rates of neurosensory impairment and mothers with higher education and were from an ethnic nonminority. CONCLUSIONS: Significant catch-up in receptive vocabulary is observed by the age of 16 years among very preterm children compared to term peers. The absence of neurosensory impairment and residing in a favorable socioeconomic milieu are associated with the most optimal developmental trajectories.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".