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Record W7007958316

Association of Birthweight Discordance with Adverse Birth Outcomes Among Live-Born Twins: A Multi-Center Study in China

2025· article· en· W7007958316 on OpenAlexaff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2025
Typearticle
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsPediatric Oncology Group
Fundersnot available
KeywordsChinaThe RepublicPeople's RepublicPregnancyBayTurkish republic
DOInot available

Abstract

fetched live from OpenAlex

Bijun Shi,1– 4 Xiaohua Tan,1 Qian Chen,1 Danfang Lu,5,* Shuhua Ren,6,* Kang Huang,7,* Wei Shen,8,* Zhifeng Chen,9,* Jin Liu,10,* Chuming You,11,* Guifang Li,12,* Hong Jiang,13,* Hongping Rao,14,* Jianwu Qiu,15,* Xian Wei,16,* Yayu Zhang,17,* Xiaobo Lin,18,* Haiyan Jiang,19,* Shasha Han,20,* Fan Wang,21,* Xiufang Yang,22,* Yitong Wang,23,* Niyang Lin,24,* Lizi Lin,25 Xinzhu Lin,8 Qiliang Cui1 1Department of Neonatology, Guangdong-Hong Kong-Macao Greater Bay Area Higher Education Joint Laboratory of Maternal-Fetal Medicine, The Third Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510150, People’s Republic of China; 2Department of Neonatology, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangzhou, 510150, People’s Republic of China; 3Department of Neonatology, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, Guangzhou, 510150, People’s Republic of China; 4Department of Neonatology, Guangzhou Key Laboratory of Neonatal Intestinal Diseases, Guangzhou, 510150, People’s Republic of China; 5Department of Pediatrics, Peking University Third Hospital, Beijing, 100000, People’s Republic of China; 6Department of Neonatology, Sichuan Jinxin Xinan Women & Children’s Hospital, Chengdu, 610001, People’s Republic of China; 7Department of Neonatology, Affiliated Hospital of Guizhou Medical University, Guiyang, 550000, People’s Republic of China; 8Department of Neonatology, Women and Children’s Hospital, School of Medicine, Xiamen University, Xiamen, 361000, People’s Republic of China; 9Department of Neonatology, The Tenth Affiliated Hospital of Southern Medical University, Dongguan, 523000, People’s Republic of China; 10Department of Neonatology, The First Affiliated Hospital of Shaoyang University, Shaoyang, 422000, People’s Republic of China; 11Department of Neonatology, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, 510000, People’s Republic of China; 12Department of Neonatology, Cangzhou People’s Hospital, Cangzhou, 061000, People’s Republic of China; 13Department of Neonatology, Yanan University Affiliated Hospital, Yan’an, 716000, People’s Republic of China; 14Department of Neonatology, Huizhou Central People’s Hospital, Huizhou, 516000, People’s Republic of China; 15Department of Neonatology, Affiliated Yuebei People’s Hospital of Shantou University Medical College, Shaoguan, 512026, People’s Republic of China; 16Department of Neonatology, Xiaogan Hospital Affiliated to Wuhan University of Science and Technology, Xiaogan, 432000, People’s Republic of China; 17Department of Neonatology, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010000, People’s Republic of China; 18Department of Neonatology, The Second Affiliated Hospital of Shantou University Medical College, Shantou, 515000, People’s Republic of China; 19Department of Neonatology, The Third Staff Hospital of Baogang Group, Baotou, 014000, People’s Republic of China; 20Department of Neonatology and Pediatrics, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, 510000, People’s Republic of China; 21Department of Neonatology, Lanzhou University Second Hospital, Lanzhou, 730000, People’s Republic of China; 22Department of Neonatology, Zhongshan City People’s Hospital, Zhongshan, 528400, People’s Republic of China; 23Department of Neonatology, The Binhaiwan Central Hospital of Dongguan, Dongguan, 523000, People’s Republic of China; 24Department of Neonatology, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515000, People’s Republic of China; 25Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, 510000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xinzhu Lin, Email xinzhufj@163.com Qiliang Cui, Email cuiql_gysy@163.comBackground: Twin pregnancies, accounting for a rising proportion of births globally, present significant public health challenges in China. Birthweight discordance (BWD), a critical complication, remains understudied in its epidemiological context, particularly regarding its population-level associations with adverse neonatal outcomes.Methods: This multi-center, retrospective cohort study leveraged data from 21 hospitals across 18 Chinese cities (2018– 2020) to assess BWD and its epidemiological implications. Ordinal logistic regression with random effects was used to explore their association. BWD was defined as: [(larger birthweight − smaller birthweight) / larger birthweight] × 100% and categorized into four grades: I (≤ 15%), II (> 15% to 20%), III (> 20% to 25%), and IV (> 25%).Results: Among 6437 twin pairs, 73.6% were classified as Grade I (no BWD), while 10.7%, 7.1%, and 8.6% constituted Grades II, III, and IV discordance, respectively. Dose-response relationships emerged: each incremental BWD elevated risks of small vulnerable newborns (aOR = 1.83, 95% CI 1.76– 1.90), small for gestational age (aOR = 1.23, 95% CI 1.18– 1.29), low birthweight (LBW, aOR = 1.16, 95% CI 1.13– 1.20), very LBW (aOR = 1.63, 95% CI 1.53– 1.73) and extreme LBW (aOR = 1.82, 95% CI 1.61– 2.05). Smaller twins exhibited disproportionately higher adverse outcome rates than larger twins. Sensitivity analyses confirmed robustness across specific subgroups.Conclusion: BWD exceeding 20% affects 15.7% of live-born twins in China, mirroring rates in high-income settings. BWD demonstrates strong dose-response relationships with adverse outcomes, validating its utility for twin health stratification. These findings call for integrating BWD assessment into prenatal surveillance and risk-adapted care to reduce neonatal morbidity/mortality, urging clinicians and policymakers to prioritize perinatal outcome equity.Keywords: twins, birthweight discordance, perinatal epidemiology, adverse birth outcomes, public health, multi-center study

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.107
GPT teacher head0.502
Teacher spread0.395 · 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 designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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