Bibliographic record
Abstract
BACKGROUND: Whether denominators for postnatal outcomes (ascertained after live birth) with a presumed prenatal origin should consist of fetuses or live births remains controversial. Proponents argue that the extended fetuses-at-risk (FAR) approach (a), provides a justification for medically indicated preterm delivery, (b), avoids paradoxical results, and (c), permits quantification of incidence of fetal-infant phenomena, such as "revealed" small for gestational age (SGA)-which, under FAR, rises with advancing gestation. METHODS: This conceptual paper examines the validity of the above arguments. RESULTS: As obstetricians induce babies early because of fetal (or maternal) compromise and despite the dangers posed by immaturity, there is no need to modify a paradigm that portrays preterm birth as a powerful risk factor. The FAR approach generally avoids "paradoxical" intersections because FAR rates of postnatal outcomes depend on the birth rate. However, this property, which causes rates of most postnatal outcomes to rise at term, can also lead to risk reversals and other misleading findings. The FAR formulation does not yield the incidence of postnatal conditions but, rather, the incidence of live birth (and survival to diagnosis) of babies with prevalent conditions (and, sometimes, future ones). CONCLUSIONS: The proposed arguments do not provide adequate support for extending the FAR approach to postnatal outcomes. As only live births can contribute to the numerator of rates, the usefulness and interpretability of FAR measures in this setting are limited.
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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 | 0.039 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.025 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".