Change in Volumes and Location of Preterm White Matter Injury over a Period of 15 Years
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether white matter injury (WMI) volumes and spatial distribution, which are important predictors of neurodevelopmental outcomes in preterm infants, have changed over a period of 15 years. STUDY DESIGN: Five hundred and twenty-eight infants born <32 weeks' gestational age from 2 sequential prospective cohorts (cohort 1: 2006 through 2012; cohort 2: 2014 through 2019) underwent early-life (median 32.7 weeks postmenstrual age) and/or term-equivalent-age MRI (median 40.7 weeks postmenstrual age). WMI were manually segmented for quantification of volumes. There were 152 infants with WMI with 74 infants in cohort 1 and 78 in cohort 2. Multivariable linear regression models examined change in WMI volume across cohorts while adjusting for clinical confounders. Lesion maps assessed change in WMI location across cohorts. RESULTS: There was a decrease in WMI volume in cohort 2 compared with cohort 1 (β = -0.6, 95% CI [-0.8, -0.3], P < .001) with a shift from more central to posterior location of WMI. There was a decrease in clinical illness severity of infants across cohorts. CONCLUSIONS: We found a decrease in WMI volume and shift to more posterior location in very preterm infants over a period of 15 years. This may potentially reflect more advanced maturation of white matter at the time of injury which may be related to changes in clinical practice over time.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".