Dual white matter pathology in fetal holoprosencephaly featuring concurrent malformative and destructive features: A case series
Bibliographic record
Abstract
Holoprosencephaly (HPE) is a classic brain malformation involving defective forebrain induction and patterning. Cases of HPE bearing white matter abnormalities have not been well documented, with only rare cases exhibiting hypoxic-ischemic damage. However, neuroradiologic studies of HPE using diffusion tensor imaging have suggested the presence of white matter architectural disarray. Described in this case series are the clinicopathologic features of 8 fetuses with HPE who underwent autopsy at BC Children's Hospital. All 8 cases exhibited subacute to chronic, periventricular leukomalacia (PVL)-like white matter pathology, with 7 of 8 cases also demonstrating aberrant white matter tracts, one of which manifested as a discreet bundle crossing the midline within the ventral aspects of the fused deep gray nuclei. In 6 of these 7 cases, the PVL-like pathology resided within this aberrant white matter tract. Original workup, alongside an additional HPE-focused next-generation sequencing panel identified a likely etiologic cause for the HPE in 4 cases, with an additional 2 cases exhibiting a variant of unknown significance in genes previously suggested to be involved in HPE. Despite our in-depth clinicopathologic and molecular review, no unifying etiology was definitively identified among our series of fetal HPE bearing this unusual pattern of white matter pathology.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".