Neonate With Necrotic Skin Lesions and Middle Cerebral Artery Stroke in the Context of Fetal Vascular Malperfusion
Bibliographic record
Abstract
There are few published reports of neonates born with necrotic skin lesions and stroke. We report the case of a neonate with an identified placental etiology for necrotic skin lesions at birth and multiple arterial thrombi, including a stroke. This neonate was born to a generally healthy mother with gestational hypertension on labetalol in pregnancy. She was born via emergency Caesarean section at 38 + 1 weeks' gestational age due to biophysical profile 2 of 8 and oligohydramnios and was noted to have multiple necrotic skin lesions on the left forearm. She was found to have numerous arterial thrombi, including a left-sided middle cerebral artery stroke. An extensive workup into the etiology of thrombi was done with no hematologic, metabolic, genetic, or infectious cause identified. Her placental pathology examination identified high-grade fetal vascular malperfusion, which encompasses various obstructive fetal vascular lesions, fetal thrombotic vasculopathy, and fetal vascular thrombi. Although fetal vascular malperfusion cannot be detected antenatally at the present time, postnatal identification can bring closure to families and help for subsequent pregnancy planning. Additionally, necrotic skin lesions in neonates present at delivery should prompt clinicians to do imaging to identify thrombi because initiating early postnatal treatment may be of critical clinical importance.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".