Biomarkers of hypoxic–ischemic encephalopathy: a systematic review
Bibliographic record
Abstract
BACKGROUND: Current diagnostic criteria for hypoxic-ischemic encephalopathy in the early hours lack objective measurement tools. Therefore, this systematic review aims to identify putative molecules that can be used in diagnosis in daily clinical practice (PROSPERO ID: CRD42021272610). DATA SOURCES: Searches were performed in PubMed, Web of Science, and Science Direct databases until November 2020. English original papers analyzing samples from newborns > 36 weeks that met at least two American College of Obstetricians and Gynecologists diagnostic criteria and/or imaging evidence of cerebral damage were included. Bias was assessed by the Newcastle-Ottawa Scale. The search and data extraction were verified by two authors separately. RESULTS: From 373 papers, 30 met the inclusion criteria. Data from samples collected in the first 72 hours were extracted, and increased serum levels of neuron-specific enolase and S100-calcium-binding protein-B were associated with a worse prognosis in newborns that suffered an episode of perinatal asphyxia. In addition, the levels of glial fibrillary acidic protein, ubiquitin carboxyl terminal hydrolase isozyme-L1, glutamic pyruvic transaminase-2, lactate, and glucose were elevated in newborns diagnosed with hypoxic-ischemic encephalopathy. Moreover, pathway analysis revealed insulin-like growth factor signaling and alanine, aspartate and glutamate metabolism to be involved in the early molecular response to insult. CONCLUSIONS: Neuron-specific enolase and S100-calcium-binding protein-B are potential biomarkers, since they are correlated with an unfavorable outcome of hypoxic-ischemic encephalopathy newborns. However, more studies are required to determine the sensitivity and specificity of this approach to be validated for clinical practice.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.008 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".