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Record W4411259719 · doi:10.1038/s41390-025-04039-4

Association between the risk of necrotizing enterocolitis and intrauterine growth: a multicenter cohort study

2025· article· en· W4411259719 on OpenAlexafffund
Dan Dang, Siyuan Jiang, Joseph Ting, Xiaoping Lei, Xinyue Gu, Wenhao Zhou, Lizhong Du, Yun Cao, Shoo Kim Lee, Hui Wu, Jianguo Zhou, Shoo K. Lee, Chao Chen, Xiuyong Cheng, Huayan Zhang, Xiuying Tian, Jingyun Shi, Zhankui Li, Chuanzhong Yang, Ling Liu, Zuming Yang, Jianhua Fu, Yong Bae Ji, Dongmei Chen, Changyi Yang, Rui Cheng, Xiaoming Peng, Ruobing Shan, Shuping Han, Lili Wang, Qiufen Wei, Mingxia Li, Yiheng Dai, Hong Jiang, Wenqing Kang, Xiaohui Gong, Xiaoyun Zhong, Yuan Shi, Shanyu Jiang, Bing Sun, Long Li, Zhenlang Lin, Jiang-Qin Liu, Jiahua Pan, Hongping Xia, Falin Xu, Yinping Qiu, Li Ma, Ling Yang, Xiaori He, Yanhong Li, Deyi Zhuang, Qin Zhang, Wenbin Dong, Jianhua Sun, Kun Liang, Huaiyan Wang, Jinxing Feng, Liping Chen, Xin-Zhu Lin, Chunming Jiang, Chuan Nie, Linkong Zeng, Mingyan Hei, Hongdan Zhu, Hongying Mi, Zhaoqing Yin, Hongxia Song, Hongyun Wang, Dong Li, Yan Gao, Yajuan Wang, Liying Dai, Liyan Zhang, Yangfang Li, Qianshen Zhang, Guofang Ding, Jimei Wang, Xiaoxia Chen, Zhen Wang, Zheng Tang, Xiaolu Ma, Xiaomei Zhang, Xiaolan Zhang, Fang Wu, Yanxiang Chen, Ying Wu

Bibliographic record

VenuePediatric Research · 2025
Typearticle
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsUniversity of TorontoMount Sinai HospitalWomen and Children’s Health Research InstituteUniversity of Alberta
FundersNational Key Research and Development Program of ChinaCanadian Institutes of Health ResearchDepartment of Science and Technology of Jilin ProvinceNational Natural Science Foundation of China
KeywordsNecrotizing enterocolitisMedicineCohortCohort studyMulticenter studyPediatricsObstetricsInternal medicine

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.004
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.032
GPT teacher head0.385
Teacher spread0.354 · 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 teacher head, 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".

Quick stats

Citations4
Published2025
Admission routes2
Has abstractno

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