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.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".