Unfolding the mystery of preterm birth
Bibliographic record
Abstract
Complications of preterm birth (PTB) are the single largest direct cause of neonatal deaths, responsible for 35% of the world's 3.1 million deaths a year, and are the second most common cause of deaths in children under 5 years of age, after pneumonia (Liu et al. Lancet 2012;379:2151–61). The study explored retrospectively the maternal, fetal, and placental conditions associated with medically indicated PTB. It included 25 699 births over 10 years using a perinatal database in London, Ontario, Canada. A random selection of one birth per mother was included. Stillbirths, delivery for maternal request, and repeat caesarean sections with no other indications for early delivery were excluded. The authors appropriately grouped the early-birth groups into medically indicated late preterm birth (MI_LPB), at 34–36 weeks of gestation, and medically indicated early-term birth (MI_ETB), at 37–38 weeks of gestation. The rate of MI_ETB (13.3%) was almost five times the rate of MI_LPB (2.7%). ETB was previously overlooked in epidemiological studies because of the underestimated risk in this group. The historical definition of preterm deliveries being less than 37 completed weeks of gestation has proven to be inaccurate. It does not account for the functional immaturity of infants born at 37–39 weeks of gestation (Goldenberg et al. Am J Obstet Gynecol 2012;206:113–8). ETBs contribute to frequent initial admissions or transfers from full-term nurseries to neonatal intensive care units (NICUs). Campbell et al. adopted the classification system of Global Alliance to Prevent Prematurity and Stillbirth (GAPPS). The ‘phenotype’ was used to describe observable properties or characteristics resulting from the interaction of the genotype with the environment. In the study the authors found a slightly different pattern of conditions for the early birth groups across the three phenotypes, in particular the maternal phenotype. Chronic maternal conditions (e.g. respiratory disease) were only associated with MI_ETB, but not with MI_LPB. The study concluded that the risks associated with medically indicated early births were heterogeneous, and that different conditions triggered the late-preterm and the early-term deliveries. As 98% of deliveries occurred in hospitals, the results are generalisable. There was overlap in assigning conditions to various phenotypes, which does not represent a limitation in the current study but is a challenge to the GAPPS classification system. These results are important as they help to identify high-risk groups in a Canadian population, and investigate causality, and thereby provide adequate preventive and therapeutic interventions. The worldwide persistence of medical conditions associated with PTB suggests genetic risks or epigenetic alterations. Those alterations could induce a multiple-hit phenomenon where environmental conditions activate a quiescent genetic abnormality, causing a cell-programmed event, such as decidual senescence or trophoblastic apoptosis, which contribute to PTB. DNA methylation patterns induced by changes in the fetal environment, such as maternal cigarette smoking, infections, stress, and diet, may influence PTB (Roberto et al. Science 2014;345:760–5) and predispose the neonate to adult-onset diseases. Although they do not alter DNA sequences, these epigenetic modifications can be heritable and carry the risk of PTB throughout future generations. None declared. Completed disclosure of interests form available to view online as supporting information. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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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 | 0.017 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.006 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".