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Record W1608358223 · doi:10.1111/1471-0528.13479

Unfolding the mystery of preterm birth

2015· letter· en· W1608358223 on OpenAlexaboutno aff

Bibliographic record

VenueBJOG An International Journal of Obstetrics & Gynaecology · 2015
Typeletter
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGestationObstetricsPediatricsIntensive careGestational agePneumoniaPregnancyInternal medicineIntensive care medicine

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.077
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.077
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0030.016
Scholarly communication0.0060.016
Open science0.0020.007
Research integrity0.0060.020
Insufficient payload (model declined to judge)0.0040.002

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.

Opus teacher head0.037
GPT teacher head0.301
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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