Bibliographic record
Abstract
Recent advances in the clinical management of at-risk pregnancy and care of the newborn have reduced morbidity and mortality among sick neonates, and improved our knowledge of factors that influence the risks of brain injury. In parallel, the refinement of imaging techniques has added to the ability of clinicians to define the etiology, timing and location of pathologic changes with diagnostic and prognostic relevance to the developing fetus and newborn infant. Abnormalities of brain growth, or injury to the developing brain can occur during pregnancy; during labor and delivery, hypoxia, acidosis and ischemia pose major risks to the fetus. Defined practices for the management of pregnancy and delivery, and evidence-based strategies for care in the newborn period are influencing outcome. However, newborn infants, especially those born prematurely, remain at risk from situations that can cause or worsen brain injury. The literature reviewed here explains the mechanisms and timing of injury, and the importance of hypoxia, ischemia, hypotension and infection; describes current diagnostic strategies, neuroimaging technologies and care entities available; and outlines approaches that can be used to prevent or mitigate brain injury. Some show particular promise, and all are relevant to lowering the incidence and severity of brain damage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".