Bibliographic record
Abstract
Brain injury in preterm infants, as demonstrated with neonatal MRI, is associated with adverse neurodevelopmental outcomes in cognitive, language, motor, educational, behavioral, and social domains.1 Such brain injury is heterogeneous, is rarely localized, and evolves over time. White matter injury (WMI) in preterm infants is by far the most frequent brain injury, and occurs in over 70% of preterm infants born before 31 weeks of gestation.2 WMI found in the preterm brain includes cystic lesions, focal small lesions with high signal intensity (best seen on T1-weighted images), diffuse excessive high signal intensity (DEHSI), and loss of white matter volume. Although classic cystic periventricular leukomalacia is becoming rarer, DEHSI is seen often, and is obvious at term-equivalent age (TEA). Its specific relevance is open for debate, with poor predictive value for 2-year outcomes.3 However, qualitative MRI analysis assessing loss of substance (white matter atrophy, ventricular dilation, and associated corpus callosum thinning) at TEA is able to predict 2-year outcomes.4 In this era of active research for new neuroprotective therapies in the neonatal period, robust comprehensive approaches are warranted not just to predict the consequences of injury, but also to identify newborns who could benefit from future targeted neuroprotective therapies. So far, only a handful of systematic qualitative or quantitative MRI approaches have been evaluated or used to assess therapeutic efficiency.5,6
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 | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".