Differential Progression of Magnetization Transfer Imaging Changes Depending on Severity of Cerebral Hypoxic-Ischemic Injury
Bibliographic record
Abstract
We hypothesized that magnetic resonance magnetization transfer (MT) imaging would be sensitive for detecting cerebral ischemic injury in white matter of neonatal brain. We compared the progression of changes in T(2) and the MT ratio (MTR) after cerebral hypoxic-ischemic insults of differing severity in neonatal rats. Magnetization transfer imaging parameters were first optimized, and then MTR and T(2) maps were acquired at various times after a mild (rather selective white matter) or substantial insult produced by unilateral cerebral hypoxia-ischemia. Depending on insult severity, time after insult, and region (e.g., subcortical white matter or cortex), cerebral hypoxia-ischemia produced reductions in MTR and an increase in T(2). The exception was acutely at 1 to 5 h at which time points MTR was reduced ipsilaterally in white matter, whereas T(2) was not affected significantly. Progression of imaging changes differed in rats grouped according to whether gross damage was present after chronic recovery. Behavioral changes were generally associated with chronic reductions in MTR and gross brain damage. Magnetization transfer imaging was capable of early detection of hypoxic-ischemic injury and particularly sensitive for identifying the progression of cerebral injury in white matter. Magnetization transfer ratio has potential for assisting with early diagnosis and treatment assessment for infants affected by perinatal hypoxia-ischemia.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".