Emerging neuroprotective interventions in periventricular leukomalacia - A systematic review of preclinical studies
Bibliographic record
Abstract
INTRODUCTION: Periventricular leukomalacia (PVL) is a result of various antenatal, intrapartum, or postnatal insults to the developing brain and is an important harbinger of cerebral palsy in preterm neonates. There is no proven therapy for PVL. This calls for appraisal of targeted therapies that have been investigated in animal models to evaluate their relevance in a clinical research context. AREAS COVERED: This systematic review identifies interventions that were evaluated in preclinical studies for neuroprotective efficacy against PVL. We identified 142 studies evaluating various interventions in PVL animal models (search method is detailed in section 2). EXPERT OPINION: Interventions that have yielded significant results in preclinical research, and that have been evaluated in a limited number of clinical trials include stem cells, erythropoietin, and melatonin. Many other therapeutic modalities evaluated in preclinical studies have been identified, but more data on their neuroprotective potential in PVL must be garnered before they can be considered for clinical trials. Because most of the tested interventions had only a partial efficacy, a combination of interventions that could be synergistic should be investigated in future preclinical studies. Furthermore, since the nature and pattern of perinatal insults to preterm brain predisposing it to PVL are substantially variable, individualized approaches for the choice of appropriate neuroprotective interventions tailored to different subgroups of preterm neonates should be explored.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".