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Record W2979170851 · doi:10.1080/01635581.2019.1670218

Association between dietary fiber and endometrial cancer: a meta-analysis

2019· review· en· W2979170851 on OpenAlexaboutno aff
Hengjie Li, Hui Mao, Yi Yu, Yong Hai Nan

Bibliographic record

VenueNutrition and Cancer · 2019
Typereview
Languageen
FieldMedicine
TopicEndometrial and Cervical Cancer Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsConfoundingMedicineConfidence intervalRelative riskMeta-analysisDemographyBody mass indexEndometrial cancerInternal medicineEnvironmental healthCancer

Abstract

fetched live from OpenAlex

To explore a potential relationship between dietary fiber consumption and risk of endometrial cancer (EC), eligible studies published up to 30 June 2018 were retrieved via computer searches and manual review of references. Random-effects models were used to calculate summary relative risk (RR) estimates based on contrasting high- and low-fiber intake values. Sensitivity analysis was conducted, and heterogeneity among study results was explored through stratified analyses by study design, geographic region, Newcastle-Ottawa Scale (NOS) score, impact factor, and adjustment for several confounders (age, body mass index, smoking, energy intake, and education). We extracted data from 16 studies (involving 6,563 cases). There was a significant association between dietary fiber intake and EC (RR = 0.86, 95% confidence interval [CI]: 0.78, 0.93). In stratified analysis, this trend was more pronounced in the case-control studies, and in studies conducted in the Americas and Asia. The relationship was further confirmed after adjusting for education level (RR = 0.74; 95% CI: 0.60, 0.88) and age (RR = 0.70; 95% CI: 0.57, 0.83), and NOS scores of 6 (RR = 0.81; 95% CI: 0.67, 0.95) and 7 (RR = 0.75; 95% CI: 0.62, 0.88). In conclusion, our meta-analysis revealed an inverse association between dietary fiber consumption and EC risk. Further efforts should be made to confirm these findings.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.771
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.222
GPT teacher head0.422
Teacher spread0.201 · 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.

Study designOther design
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

Citations17
Published2019
Admission routes1
Has abstractyes

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