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Record W4282933379 · doi:10.1158/1538-7445.am2022-3652

Abstract 3652: Maternal body mass index, diabetes, and gestational weight gain and risk for pediatric cancer in offspring: A systematic review and meta-analysis

2022· review· en· W4282933379 on OpenAlexaboutno aff
Andrew R. Marley, Allison Domingues, Taumoha Ghosh, Lucie M. Turcotte, Logan G. Spector

Bibliographic record

VenueCancer Research · 2022
Typereview
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGestational diabetesMeta-analysisBody mass indexCancerOffspringPregnancyObesityPediatricsObstetricsInternal medicineGestation

Abstract

fetched live from OpenAlex

Abstract Introduction: Pediatric cancer incidence has steadily increased concurrent with the rise in adult obesity, however, associations between maternal obesity and associated comorbidities and pediatric cancer risk remain understudied. Methods: A comprehensive and systematic literature search in PubMed and EMBASE databases from their inception to March 15th, 2021. Eligible studies reported risk estimates, sample sizes, and provided sufficient description of outcome and exposure ascertainment. Studies were excluded if not complete, published, peer-reviewed studies, or if provided incompatible exposure data. Data quality was assessed according to Newcastle-Ottawa Scale (NOS) guidelines and reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Random-effects models were used to estimate pooled effects. Results: Thirty-four studies were included, covering 3,404,747 subjects and 14,706 pediatric cancer cases for pre-pregnancy BMI; 14,748,772 subjects and 44,628 pediatric cancer patients for maternal diabetes; and 2,124,647 participants and 15,915 pediatric cancer cases for gestational weight gain. Pre-pregnancy BMI was significantly associated with leukemia risk in offspring (OR per 5-unit BMI increase =1.07 [95% CI = 1.04-1.11] I2 = 0.0%). Any maternal diabetes was positively associated with acute lymphoblastic leukemia risk (OR=1.46 [95% CI = 1.28-1.67] I2 = 0.0%), even after restricting to birthweight-adjusted analyses (OR [95% CI] = 1.74 [1.29-2.34] I2 = 0.0%), and inversely associated with risk of central nervous system tumors (OR=0.73 [95% CI = 0.55-0.97] I2 = 0.0%). Pre-gestational diabetes (OR=1.57 [95% CI = 1.11-2.24] I2 = 26.8%) and gestational diabetes (OR=1.40 [95% CI = 1.12-1.75] I2 = 0.0%) were also significantly associated with acute lymphoblastic leukemia risk. No significant associations were observed for gestational weight gain and pediatric cancer risk. Conclusions: Maternal obesity and diabetes may be etiologically linked to pediatric cancer, particularly leukemia and central nervous system tumors. Our findings support appropriate weight management and glycemic control as important components of maternal care and offspring health. Further validation studies are warranted. Citation Format: Andrew R. Marley, Allison Domingues, Taumoha Ghosh, Lucie M. Turcotte, Logan G. Spector. Maternal body mass index, diabetes, and gestational weight gain and risk for pediatric cancer in offspring: A systematic review and meta-analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3652.

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)0.011
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.030
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0160.032
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.137
GPT teacher head0.446
Teacher spread0.309 · 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 designMeta-analysis
Domainnot available
GenreReview

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
Published2022
Admission routes1
Has abstractyes

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