Implications of Using a Fetuses‐at‐Risk Approach When Fetuses Are Not at Risk
Bibliographic record
Abstract
BACKGROUND: Gestational-age-specific rates of postnatal endpoints are sometimes estimated with denominators based on fetuses-at-risk (FAR), rather than live births. However, as infants can only be included in the numerator after they are born alive, interpretation of such rates is problematic. METHODS: Using simple algebra it can be shown that, at each gestational week, FAR rates of postnatal endpoints are the product of the conventional risk of outcome among live births and the probability of live birth, which increases from near zero early in gestation to close to one in the final weeks. The consequences of such a pattern of live birth on FAR rates are further illustrated in hypothetical scenarios with known conditions. RESULTS: FAR rates of postnatal endpoints will generally increase towards the end of pregnancy due to the rising probability of live birth, regardless of the 'true' effect of immaturity on risk. In the presence of an exposure that increases the probability of early birth, the same mechanism will cause FAR rates to be higher in the exposed group, even if the exposure has no effect. CONCLUSIONS: Gestational-age-specific FAR rates of postnatal outcomes strongly depend on the probability of live birth. Thus, they reflect neither the causal effect of gestational length, nor that of a given exposure. Indeed, if an exposure shortens gestation, FAR rates will be higher in exposed infants even when the exposure has no impact on the outcome under study. These intrinsic limitations should be taken into account when applying FAR analyses to postnatal endpoints.
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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 | 0.076 | 0.195 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".