Comparison of Computer Tomography and Magnetic Resonance Imaging Scans on the Third Day of Life in Term Newborns With Neonatal Encephalopathy
Bibliographic record
Abstract
OBJECTIVE: Our goal was to compare the patterns of brain injury detected by computed tomography, conventional MRI (T1- and T2-weighted sequences), and diffusion-weighted MRI in a cohort of term newborns with neonatal encephalopathy studied uniformly with all 3 modalities on the third day of life. METHODS: Term newborns (> or =36 weeks' gestation) admitted to our center with neonatal encephalopathy were scanned with computed tomography, MRI, and diffusion-weighted MRI at 72 (+/-12) hours of life (n = 48). Each modality was scored independently of the other with previously validated scoring systems. The predominant pattern of brain injury was classified as: normal, watershed, basal nuclei, total (maximal basal nuclei and watershed), and focal-multifocal (presence of strokes and/or white matter injury alone). RESULTS: The agreement for the predominant pattern of injury was excellent between MRI and diffusion-weighted MRI (77% agreement). The agreement for the pattern of injury was also good for computed tomography and diffusion-weighted MRI (67% agreement). The extent of cortical injury and focal-multifocal lesions, such as strokes and white matter injury, were less apparent on computed tomography than diffusion-weighted MRI. In 19 newborns with a repeat MRI in the second week of life, the predominant pattern seen on the day 3 diffusion-weighted MRI was confirmed. CONCLUSIONS: Diffusion-weighted MRI is the most sensitive technique with which to assess brain injury on day 3 of life in term newborns with neonatal encephalopathy, particularly for cortical injury and focal-multifocal lesions such as stroke and white matter injury. All 3 modalities identify the most serious patterns of brain injury similarly.
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 | 0.000 | 0.000 |
| 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".