Impact of maternal posture on fetal physiology in human pregnancy: a narrative review
Bibliographic record
Abstract
In numerous medical conditions, including pregnancy, gravity and posture interact to impact physiology and pathophysiology. Recent investigations, for example, pertaining to maternal sleeping posture during the third trimester and possible impact on fetal growth and stillbirth risk highlight the importance and potential clinical implications of the subject. In this review, we provide an extensive discussion of the impact of maternal posture on fetal physiology from conception to the postpartum period in human pregnancy. We conducted a systematic literature search of the MEDLINE database and identified 242 studies from 1991 through 2021, inclusive, that met our inclusion criteria. Herein, we provide a synthesis of the resulting literature. In the first section of the review, we group the results by the impact of maternal posture at rest on the cervix, uterus, placenta, umbilical cord, amniotic fluid, and fetus. In the second section of the review, we address the impact on fetal-related outcomes of maternal posture during various maternal activities (e.g., sleep, work, exercise), medical procedures (e.g., fertility, imaging, surgery), and labor and birth. We present the published literature, highlight gaps and discrepancies, and suggest future research opportunities and clinical practice changes. In sum, we anticipate that this review will shed light on the impact of maternal posture on fetal physiology in a manner that lends utility to researchers and clinicians who are working to improve maternal, fetal, and child 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".