Quantitative assessment of white matter injury in preterm neonates: Association with outcomes.
Bibliographic record
Abstract
OBJECTIVE: To quantitatively assess white matter injury (WMI) volume and location in very preterm neonates, and to examine the association of lesion volume and location with 18-month neurodevelopmental outcomes.\nMETHODS: Volume and location of WMI was quantified on MRI in 216 neonates (median gestational age 27.9 weeks) who had motor, cognitive, and language assessments at 18 months corrected age (CA). Neonates were scanned at 32.1 postmenstrual weeks (median) and 68 (31.5%) had WMI; of 66 survivors, 58 (87.9%) had MRI and 18-month outcomes. WMI was manually segmented and transformed into a common image space, accounting for intersubject anatomical variability. Probability maps describing the likelihood of a lesion predicting adverse 18-month outcomes were developed.\nRESULTS: WMI occurs in a characteristic topology, with most lesions occurring in the periventricular central region, followed by posterior and frontal regions. Irrespective of lesion location, greater WMI volumes predicted poor motor outcomes (\nCONCLUSIONS: The predictive value of frontal lobe WMI volume highlights the importance of lesion location when considering the neurodevelopmental significance of WMI. Frontal lobe lesions are of particular concern.
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 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".