Grey and white matter volume changes after preterm birth: A meta-analytic approach
Bibliographic record
Abstract
ABSTRACT Objective Lower brain grey matter volumes (GMV) and white matter volumes (WMV) have been reported at single time points in preterm-born individuals. While large MRI studies in the normative population have led to a better understanding of brain growth trajectories across the lifespan, such results remain elusive for preterm-born individuals since large, aggregated datasets of preterm-born individuals do not exist. To close this gap, we investigated GMV and WMV in preterm-born individuals as reported in the literature and contrasted it against individual volumetric data and trajectories from the general population. Study design Systematic database search of PubMed and Web of Science in March 2021 and extraction of outcome measures by two independent reviewers. Individual data on full-term controls was extracted from freely available databases. Mean GMV, WMV, total intracranial volume (TIV), and mean age at scan were the main outcome measures. Results Of 532 identified records, nine studies were included with 538 preterm-born subjects between 1.1 and 28.5 years of age. Reference data was generated from 880 full-term controls between 1 and 30 years of age. GMV was consistently lower in preterm-born individuals from infancy to early adulthood with no evidence for catch-up growth. While GMV changes followed a similar trajectory as full-term controls, WMV was particularly low in adolescence after preterm birth. Conclusions Results demonstrate altered brain volumes after premature birth across the first half of lifespan with particularly low white matter volumes in adolescence. Future studies should address this issue in large aggregated datasets of preterm-born individuals.
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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 | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.035 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".