Rationalizing Definitions and Procedures for Optimizing Clinical Care and Public Health in Fetal Death and Stillbirth
Bibliographic record
Abstract
Despite the recent focus on stillbirth, there remains a profound need to address problems associated with the definitions and procedures related to fetal death and stillbirth. The current definition of fetal death, first proposed in 1950, needs to be updated to distinguish between the timing of fetal death (which has etiologic and prognostic significance) and the timing of stillbirth (ie, the delivery of the dead fetus). Stillbirth registration procedures, modeled after live birth registration and not death registration, also need to be modernized because they can be an unnecessary burden on some grieving families. The problems associated with fetal death definitions and stillbirth-associated procedures are highlighted by selective fetal reduction in multifetal pregnancy; in many countries, the fetus reduced at 10-13 weeks of gestation and delivered at term gestation requires stillbirth registration and a burial permit even if fetal remains cannot be identified. An international consensus is needed to standardize the definition of reportable fetal deaths; ideally this should be based on the timing of fetal death and should address the status of pregnancy terminations. In this article, we list propositions for initiating an international dialogue that will rationalize fetal death definitions, registration criteria, and associated procedures, and thereby improve clinical care and public health.
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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 | 0.327 | 0.350 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.009 | 0.068 |
| Scholarly communication | 0.021 | 0.027 |
| Open science | 0.009 | 0.021 |
| Research integrity | 0.014 | 0.031 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".