Extremely preterm children exhibit altered cortical thickness in language areas
Bibliographic record
Abstract
Children born extremely preterm (< 28 weeks gestation, EPT) are at increased risk for language and other neurocognitive deficits compared to term controls (TC). Prior studies have reported both increases and decreases in cortical thickness in EPT across the cerebrum. These studies have not formally normalized for intracranial volume (ICV), which is especially important as EPT children often have smaller stature, head size, and ICV. We previously reported increased interhemispheric functional and structural connectivity in a well-controlled group of school-aged EPT children with no known brain injury or neurological deficits. Functional and structural hyperconnectivity between left and right temporoparietal regions was positively related with language scores in EPT, which may be reflected in measures of cortical thickness. To characterize possible language network cortical thickness effects, 15 EPT children and 15 TC underwent standardized assessments of language and structural magnetic resonance imaging at 4 to 6 years of age. Images were subjected to volumetric and cortical thickness analyses using FreeSurfer. Whole-brain analyses of cortical thickness were conducted both with and without normalization by ICV. Non-normalized results showed thinner temporal cortex for EPT, while ICV-normalized results showed thicker cortical regions in the right temporal lobe (FDRq = 0.05). Only ICV-normalized results were significantly related to language scores, with right temporal cortical thickness being positively correlated with performance.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".