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Record W3035708321 · doi:10.3390/medicina56060285

Incidence of Prostate Cancer in Inflammatory Bowel Disease: A Meta-Analysis

2020· review· en· W3035708321 on OpenAlexaboutno aff
Edoardo Carli, Gian Paolo Caviglia, Rinaldo Pellicano, Sharmila Fagoonee, Stefano Rizza, Marco Astegiano, Giorgio Maria Saracco, Davide Giuseppe Ribaldone

Bibliographic record

VenueMedicina · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsnot available
Fundersnot available
KeywordsIncidence (geometry)Prostate cancerMedicineInflammatory bowel diseaseMeta-analysisDiseaseCancerOncologyProstateInternal medicine

Abstract

fetched live from OpenAlex

Background and objectives: Inflammatory bowel disease (IBD) is associated with an increased risk of developing colorectal cancer as well as some extra-intestinal tumors, but there are still limited data about the risk of prostate cancer (PC). To analyze if there is an increased risk of PC in patients affected by IBD, we performed a systematic review with meta-analysis. Materials and Methods: A Pubmed search of all studies comparing standardized incidence ratio (SIR) or odds ratio (OR) or relative risks (RR) of PC between IBD and non IBD groups, published until March 2020 was conducted. The study protocol was registered on PROSPERO. Twelve studies, mostly population studies, were included. The quality score of these studies, evaluated by the Newcastle–Ottawa Scale, was 7. The heterogeneity was high among the studies in which ulcerative colitis (UC) was considered separate from Crohn’s disease (CD) and in the studies that considered UC and CD together (“IBD-studies”), while it was low in the studies which considered CD separate from UC. Results: The relative risk of developing PC was 1.71 (95% confidence interval [CI] 1.16–2.51, p = 0.007) in IBD, 1.10 (95%CI 0.98–1.25, p = 0.116) in CD, and 1.22 (95%CI 0.98–1.51, p = 0.07) in UC. Conclusions: Patients with IBD appear to have a slightly increased risk of PC compared to the general population.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.710
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.027
GPT teacher head0.324
Teacher spread0.297 · 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 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

Citations16
Published2020
Admission routes1
Has abstractyes

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