Routine Metabolic Testing is Not Warranted in Unexpected Infant Death Investigations
Bibliographic record
Abstract
Inborn errors of metabolism (IEM) only rarely cause sudden unexpected infant death. Yet, postmortem metabolic screening is often ordered reflexively during infant death investigations, even in the absence of historical, clinical or autopsy findings suggestive of IEM. This retrospective descriptive study examines the impact of metabolic screening of infants who die suddenly in a medical examiner's jurisdiction. The study population included 135 cases, one of which was certified as death due to IEM with historical and pathologic findings suggestive of IEM and an abnormal postmortem screening study, one which was certified as death due to IEM with historical and pathologic findings suggestive of IEM and a negative postmortem screening study, and one which was certified as undetermined with pathologic features of IEM and a negative postmortem screening study, but also with features suggestive of accidental asphyxia. Nine cases had abnormal postmortem screens that were deemed to represent false positives. During the entire nine-year study of these 135 cases, the utilization of screening tests in cases without historical or autopsy features of IEM did not detect any unsuspected cases. IEM may rarely cause unexpected infant death, and it can be suggested by historical and autopsy findings. Thus, within the appropriate investigative and autopsy context, judicious use of metabolic screening tests is warranted. Caution is advised when interpreting negative screening studies with suggestive historical and/or autopsy findings as the success of testing decreases with increasing postmortem interval.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".