Prognostic Tests in Term Neonates With Hypoxic-Ischemic Encephalopathy: A Systematic Review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Hypoxic-ischemic encephalopathy (HIE) after perinatal asphyxia in term neonates causes long-term neurologic sequelae or death. A reliable evidence-based prognosis is essential. The study goal was to investigate the prognostic value of currently used clinical tests in neonatal patients with perinatal asphyxia and HIE. METHODS: Searches were made on MEDLINE, Embase, Central, and CINAHL for studies occurring between January 1980 and November 2011. Studies were included if they (1) evaluated outcome in term infants with perinatal asphyxia and HIE, (2) evaluated prognostic tests, and (3) reported outcome at a minimal follow-up age of 18 months. Study selection, assessment of methodologic quality, and data extraction were performed by 3 independent reviewers. Pooled sensitivities and specificities of investigated tests were calculated when possible. RESULTS: Of the 259 relevant studies, 29 were included describing 13 prognostic tests conducted 1631 times in 1306 term neonates. A considerable heterogeneity was noted in test performance, cut-off values, and outcome measures. The most promising tests were amplitude-integrated electroencephalography (sensitivity 0.93, [95% confidence interval 0.78-0.98]; specificity 0.90 [0.60-0.98]), EEG (sensitivity 0.92 [0.66-0.99]; specificity 0.83 [0.64-0.93]), and visual evoked potentials (sensitivity 0.90 [0.74-0.97]; specificity 0.92 [0.68-0.98]). In imaging, diffusion weighted MRI performed best on specificity (0.89 [0.62-0.98]) and T1/T2-weighted MRI performed best on sensitivity (0.98 [0.80-1.00]). Magnetic resonance spectroscopy demonstrated a sensitivity of 0.75 (0.26-0.96) with poor specificity (0.58 [0.23-0.87]). CONCLUSIONS: This evidence suggests an important role for amplitude-integrated electroencephalography, EEG, visual evoked potentials, and diffusion weighted and conventional MRI. Given the heterogeneity in the tests' performance and outcomes studied, well-designed large prospective studies are needed.
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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 | 0.008 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".