Stillbirth Investigations: An Iconographic and Concise Diagnostic Workup in Perinatal Pathology
Bibliographic record
Abstract
Abstract Introduction Stillbirth is a dramatic event for the parents, health care team, and anyone close to the expectant parents. Multidisciplinary team (MDT) meetings are essential to improve communication in health care. We review the most frequent findings discussed at MDT meetings. Methods A PubMed search was conducted through December 2021 since the inception (1965) using clinical queries with the key terms “stillbirth” AND “investigation” AND “pathology” AND “human.” The search strategy included reviews, meta-analyses, randomized controlled trials, clinical trials, and observational studies. This systematic review is based on, but not limited to, the search results. It is the experience of more than 30 years of pediatrics, obstetrics, and pathology staff. Results Two hundred and six articles were screened and complemented through the perusal of congressional activities and personal communications. Pathological findings following perinatal death can be divided into macroscopic, histologic, and placental findings. The placenta is crucial in fetal medicine and is key in determining the cause of stillbirth in a substantial number of events. Perinatal lung disease is essential to evaluate the response of newborns to extrauterine life and address newborns' outcomes appropriately. Conclusions Stillbirth remains one of the less explored areas of medicine, and we can determine the cause in a limited number of cases. Nevertheless, placental pathology is critical in the etiology discovery pathway. Accurate investigations and discussion of photography-supported findings are vital in promoting communication at MDT meetings.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.020 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".