Bibliographic record
Abstract
Since the midtwentieth century, stillbirths (late fetal deaths) and early neonatal deaths have often been combined into a single category of "perinatal" deaths. In the past, such a combination was justified by the fact that asphyxia was a common cause of death during labor (intrapartum stillbirth) and shortly after birth and by geographic and temporal differences in classification of livebirths versus stillbirths. In more recent years, however, the etiologic determinants have diverged sharply, with many fewer early neonatal deaths caused by asphyxia and relatively many more caused by congenital anomalies. Moreover, the increasingly common stratification of pregnancy outcome measures by gestational age or birth weight leads to the use of an inappropriate denominator (total livebirths plus stillbirths within each gestational age or birth weight category) for denoting risk for the stillbirth component, because all unborn fetuses (including the majority of those not born within the specified gestational age or birth weight range) are at risk of being stillborn in that range. The authors suggest that, whenever possible, stillbirths and early neonatal deaths should be reported separately, with gestational age-specific risks of stillbirth based on all fetuses at risk, and that antepartum and intrapartum stillbirths be reported separately.
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 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 | 0.087 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".