Neonatal neuroimaging findings in inborn errors of metabolism
Bibliographic record
Abstract
Individually, metabolic disorders are rare, but overall they account for a significant number of neonatal disorders affecting the central nervous system. The neonatal clinical manifestations of inborn errors of metabolism (IEMs) are characterized by nonspecific systemic symptoms that may mimic more common acute neonatal disorders like sepsis, severe heart insufficiency, or neonatal hypoxic-ischemic encephalopathy. Certain IEMs presenting in the neonatal period may also be complicated by sepsis and cardiomyopathy. Early diagnosis is mandatory to prevent death and permanent long-term neurological impairments. Although neuroimaging findings are rarely specific, they play a key role in suggesting the correct diagnosis, limiting the differential diagnosis, and may consequently allow early initiation of targeted metabolic and genetic laboratory investigations and treatment. Neuroimaging may be especially helpful to distinguish metabolic disorders from other more common causes of neonatal encephalopathy, as a newborn may present with an IEM prior to the availability of the newborn screening results. It is therefore important that neonatologists, pediatric neurologists, and pediatric neuroradiologists are familiar with the neuroimaging findings of metabolic disorders presenting in the neonatal time period.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| 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 | 0.002 |
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".