Sudden Unexpected Death in Neonates: A Clinico-pathological Study
Bibliographic record
Abstract
Clinico-pathological studies that focus on sudden unexpected death (SUD) in the neonatal period are rare. The objective of this study was to elucidate the frequency and pathological spectrum of anatomical causes of death (CODs), found in the setting of sudden unexpected death in neonates (SUD-N), and to correlate the COD with premortem circumstantial information. We conducted a detailed review of all autopsy reports on SUD-N cases at our institution from 1997 to 2015. Analyzed clinical data included obstetrical history, postpartum/neonatal medical course, and circumstances surrounding death. Evaluated autopsy data included growth parameters, pathological findings, ancillary test results, and COD. Data from decedents in which a COD was established (COD-E) were statistically compared with that from decedents in which the COD was undetermined (COD-U). Of 104 neonates (M: 49; F: 55) who fulfilled our inclusion criteria, a COD was established at autopsy in 46 cases (44%). Infections, congenital abnormalities, and inborn errors of metabolism were the most common CODs. Single variables statistically more likely to be found in COD-E neonates were clinical history of prodromal illness, witnessed loss of vital signs, and evidence of physiological stress in the thymus or the liver. A prodrome was statistically more common in the COD-E group, but the absence of a prodrome does not reliably exclude COD-E cases, since over 50% of these patients were asymptomatic prior to their demise. In COD-U neonates, the statistically significant factors were death during sleep, death during sleep while "bed"-sharing, "heavy" lungs, and petechial hemorrhages on the epicardium or pleura. Given the frequency and wide spectrum of underlying pathologies in COD-E neonates, referral of SUD-N cases to pathologists with specialized pediatric autopsy expertise is recommended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".