Neuroradiological Mimics of Periventricular Leukomalacia
Bibliographic record
Abstract
AIM: Periventricular leukomalacia (PVL) is a term reserved to describe white matter injury in the premature brain. In this review article, the authors highlight the common and rare pathologies mimicking the chronic stage of PVL and propose practical clinico-radiological criteria that would aid in diagnosis and management. METHODS AND RESULTS: The authors first describe the typical brain MRI (magnetic resonance imaging) features of PVL. Based on their clinical presentation, pathologic entities and their neuroimaging findings were clustered into distinct categories. Three clinical subgroups were identified: healthy children, children with stable/nonprogressive neurological disorder, and those with progressive neurological disorder. The neuroradiological discriminators are described in each subgroup with relevant differential diagnoses. The mimics were broadly classified into normal variants, acquired, and inherited disorders. CONCLUSIONS: The term "PVL" should be used appropriately as it reflects its pathomechanism. The phrase "white matter injury of prematurity" or "brain injury of prematurity" is more specific. Discrepancies in imaging and clinical presentation must be tread with caution and warrant further investigations to exclude other possibilities.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".