Intrauterine and Neonatal Deaths
Bibliographic record
Abstract
ABSTRACT: Forensic investigation of intrauterine and perinatal deaths can be challenging, given their relative infrequency and the possible involvement of maternal substance use, trauma, and socioeconomic factors. Intrauterine and perinatal deaths investigated by the Cuyahoga County Medical Examiner's Officer between 2013-2023 were reviewed. One hundred twenty-eight cases were identified (83 stillborn and 45 live births). The predominant indications for referral were concern for maternal substance use (57.8%) or trauma (35.2%). Gestational ages ranged from 11.5 to 42.5 weeks; 36.7% were <22 weeks, and only 10.2% were full term (>37 weeks). The maternal age range was 16-41 years, with most (65.2%) between 20-34 years. Not all case files included obstetric history or home address; of those which did, 53.0% received no prenatal care and 81.7% came from zip codes in the bottom quartile of household incomes. Causes of death included acute and/or chronic maternal substance use (28.9%), chorioamnionitis (21.9%), and abruption (19.5%). Manners of death (when applicable) included natural (53.3%), accident (26.7%), homicide (8.9%), and undetermined/unassigned (11.1%). Potentially confounding socioeconomic factors were identified in most cases. This demonstrates the importance of considering these factors and exercising caution when assigning specific causes and manners to intrauterine and perinatal deaths.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".