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Record W3111452541 · doi:10.21037/tau-20-1020

Does inflammatory bowel disease increase the risk of lower urinary tract tumors: a meta-analysis

2021· article· en· W3111452541 on OpenAlexaboutno aff
Chi Zhang, Shengzhuo Liu, Jiapei Wu, Xiao Zeng, Yiping Lu, Hong Shen, Deyi Luo

Bibliographic record

VenueTranslational Andrology and Urology · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInternal medicineProstate cancerIncidence (geometry)Ulcerative colitisInflammatory bowel diseaseGastroenterologyCancerRisk factorColorectal cancerDiseaseCrohn's diseaseBladder cancerUrinary systemMeta-analysisOncology

Abstract

fetched live from OpenAlex

BACKGROUND: Inflammatory bowel disease, including ulcerative colitis and Crohn's disease, is characterized by chronic inflammation that could be a risk factor for extraintestinal cancer. The aim of this study is to evaluate whether inflammatory bowel disease is related to the risk of lower urinary tract tumors. METHODS: A systematical research was performed on various medical databases including PubMed, the Cochrane Library, Embase and Web of Science from inception to April 2020. Data were independently extracted by two reviewers. The Newcastle-Ottawa Scale and the Oxford Centre for Evidence-Based Medicine criteria were used to assess the quality of included articles. The analysis was completed by STATA version 14.2. RESULTS: =73.5%). CONCLUSIONS: Inflammatory bowel disease did not significantly increase the risk of prostate cancer, bladder cancer and male genital cancer. Crohn's disease patients seemed to have a higher risk of prostate cancer and bladder cancer, and ulcerative colitis patients seemed to have a higher risk of prostate cancer. ulcerative colitis patients in East Asian countries have significantly increased prostate cancer risk.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.622

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.228
Teacher spread0.219 · 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

Citations15
Published2021
Admission routes1
Has abstractyes

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