Placental Abruption and Perinatal Mortality: Abnormal Placentation and Spontaneous Abortion as Contributors to Left Truncation Bias
Bibliographic record
Abstract
BACKGROUND: Generally, studies in perinatal epidemiology restrict cohort entry to 20 weeks of gestation, but exposures and outcomes may occur earlier. This restriction may introduce left truncation bias. OBJECTIVES: To examine the impact of left truncation bias when estimating the causal effect of abruption on perinatal mortality in the context of abnormal placentation, with spontaneous abortion (SAB) as a censoring event. METHODS: Through 80 Monte Carlo simulation scenarios based on realistic clinical assumptions, we estimated risk differences (RD), risk ratios (RR) and bias parameters for the abruption-perinatal mortality association. RESULTS: Censoring by SAB ranged from 5.6% to 7.6% across simulation setups. The risk of mortality was overestimated in observable (left-truncated) data at ≥ 20 weeks compared to an unobservable cohort starting follow-up at placental implantation (conception cohort). Underestimation of risks was stronger among abruption pregnancies. RDs for the abruption-mortality association were biased by +1% to +3% among conceptions with normal implantation and by +5% to +43% among abnormal placentation. Due to the disproportionate underestimation of mortality among nonabruption pregnancies, RRs were overestimated by 1.1 to 1.2-fold for normal implantations and by 1.1 to 8.4-fold for abnormal implantations. CONCLUSIONS: The findings of this simulation study highlight the critical importance of placentation in successful pregnancy. Abnormal placentation has profound consequences for unsuccessful pregnancies, remarkably increasing the risks of early losses, placental abruption and other obstetrical complications. This study underscores that left truncation can bias the abruption-perinatal mortality association, differentially by whether the placentation was normal or abnormal. However, defining the causal question regarding the abruption-perinatal mortality association requires consideration of the target population, which may include all conceptions. In studies of these effects, outcome follow-up capability may introduce left truncation bias. We do not prescribe one analytic approach to account for left truncation, but rather, the approach should be guided by the causal question.
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 | 0.078 | 0.233 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".