Serial quantitative diffusion tensor MRI of the premature brain: Development in newborns with and without injury
Bibliographic record
Abstract
PURPOSE: To determine the change over time of the apparent diffusion coefficient (ADC) and relative anisotropy of cerebral water in a cohort of premature newborns serially studied near birth and again near term. MATERIALS AND METHODS: Newborns were classified as normal (N = 11), minimal white matter injury (N = 7), or moderate white matter injury (N = 5). RESULTS: ADC decreased significantly with age in all brain regions in newborns classified as normal and those with minimal white matter injury. ADC increased with age or failed to decline in widespread areas of white matter in newborns with moderate white matter injury. Anisotropy increased with age in all white matter regions in newborns classified as normal. Anisotropy did not increase in frontal white matter in those with minimal white matter injury, and in widespread white matter areas in those with moderate white matter injury. CONCLUSION: This study demonstrates that serial diffusion tensor magnetic resonance imaging scans of premature newborns can detect differences in white matter maturation in infants with and without white matter injury.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".