Comparative performance of head ultrasound and MRI in detecting preterm brain injury and predicting outcomes: A systematic review
Bibliographic record
Abstract
AIM: To systematically review the literature to compare the performance of head ultrasound (HUS) and magnetic resonance imaging (MRI) in their ability to detect brain injury and their predictive value for neurodevelopmental outcomes. METHODS: This was a systematic review based on literature search in three electronic databases (MEDLINE, EMBASE, Cochrane Library) and additional sources for studies on routine brain injury screening in preterm neonates published during 2000-May 2020. Studies were included if they reported on the comparative performance of HUS and MRI in detecting preterm brain injury and/or their predictive value for neurodevelopmental outcomes. Findings from the included studies underwent narrative synthesis. RESULTS: Forty-six studies were included. In comparison with HUS, MRI detected more anomalies and provided more details on the severity and the extent of preterm brain injury, particularly for white matter injury and cerebellar haemorrhage. Neonatal neuroimaging predicted outcomes with high negative predictive value but relatively low positive predictive value. The prognostic value of neonatal neuroimaging varied according to several factors including modality and timing of imaging, and tools used for grading brain injury. CONCLUSION: Compared with HUS, MRI offers a better characterisation of preterm brain injury and may enhance the ability to predict neurodevelopmental outcomes.
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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 | 0.009 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".