Maternal shift work during pregnancy and cardiovascular health impacts on mother and offspring
Bibliographic record
Abstract
Cardiovascular disease (CVD) is the leading cause of death for women worldwide. One of the risk factors for CVD in women is complications during pregnancy. Pregnancy complications include a wide arena of pathologies, including hypertension, preeclampsia, gestational diabetes, preterm delivery and miscarriage. Interestingly, increased evidence in recent years highlights a novel link between maternal shift work during pregnancy and increased risk for pregnancy complications, specifically hypertension and diabetes, while knowledge on other CVDs, such heart failure, atherosclerosis, ischemic heart disease, and stroke in pregnant shift working mothers is still scarce. Notably, shift work during pregnancy results in significant changes to the circadian rhythm of both the mother and fetus, therefore, engaging into shift work during pregnancy may adversely affect the cardiovascular health of both the mother and offspring, and carry into adulthood. Herein, we highlight the novel relationship between maternal shift work during pregnancy and the increased risk for pregnancy complications that may increase risk for CVD later in life. Furthermore, we provide mechanistic insights of the hemodynamic processes that are disrupted in response to maternal shift work and may explain the increased risk for cardiovascular disease. Understanding how shift work during pregnancy influences the prevalence for heart disease is of paramount clinical importance for minimizing the risk for cardiovascular disease for both the mother and offspring.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".