Early Pathological and Magnetic Resonance Detection of Cerebral Injury Using a Rat Model of Neonatal Hypoxic Ischemic Encephalopathy
Bibliographic record
Abstract
Perinatal hypoxic-ischemic encephalopathy (HIE) is an acute disease that may afflict newborns, resulting in variable long- and short-term neurodevelopmental outcomes. Early diagnosis is critical to identifying infants who may benefit from intervention; however, early diagnosis relies heavily on clinical criteria. No molecular or radiological tests have shown promise in detecting early cerebral injury. Studies have shown that magnetic resonance imaging (MRI) can show changes in both blood flow/ischemia and metabolic disruption. However, they have all been used to evaluate the secondary phase of the disease (>12 h) after the onset of the injury. Early diagnosis is critical to rapidly starting therapeutic hypothermia in eligible infants, which is currently recommended to be initiated within 6 h of birth. The rat model of hypoxic-ischemic injury was developed in 1981 and has been validated and used extensively to study changes in brain perfusion, cerebral injury markers, and morphology. However, it has primarily been used as a "late model", evaluating injury several days after the initial ischemic insult. The model has been known to have poor sensitivity in evaluating reliable and reproducible early cerebral changes. The objective of this study was to develop a reliable model to study early gross morphological and radiological markers of HIE using pathological staining and cerebral magnetic resonance imaging/magnetic resonance spectroscopy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".