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Record W3156677615 · doi:10.3390/nu13041288

Role of Dietary Habits in the Prevention of Diverticular Disease Complications: A Systematic Review

2021· review· en· W3156677615 on OpenAlexaboutno aff
Marilia Carabotti, Francesca Falangone, Rosario Cuomo, Bruno Annibale

Bibliographic record

VenueNutrients · 2021
Typereview
Languageen
FieldMedicine
TopicDiverticular Disease and Complications
Canadian institutionsnot available
Fundersnot available
KeywordsDiverticular diseaseMedicineDiverticulitisDiverticulosisInternal medicineDietary fiberDiseaseFood science

Abstract

fetched live from OpenAlex

Recent evidence showed that dietary habits play a role as risk factors for the development of diverticular complications. This systematic review aims to assess the effect of dietary habits in the prevention of diverticula complications (i.e., acute diverticulitis and diverticula bleeding) in patients with diverticula disease. PubMed and Scopus databases were searched up to 19 January 2021, 330 records were identified, and 8 articles met the eligibility criteria and were subjected to data extraction. The quality of the studies was evaluated by the Newcastle-Ottawa quality assessment form. No study meets the criteria for being a high-quality study. A high intake of fiber was associated to a decreased risk of diverticulitis or hospitalization due to diverticular disease, with a protective effect for fruits and cereal fiber, but not for vegetable fiber; whereas, a high red meat consumption and a generally Western dietary pattern were associated with an increased risk of diverticulitis. Alcohol use seemed to be associated to diverticular bleeding, but not to recurrent diverticulitis or diverticular complications. Further high-quality studies are needed to better define these associations. It is mandatory to ascertain the role of dietary habits for the development of recurrent acute diverticulitis and diverticular bleeding.

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)0.003
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.005
Bibliometrics0.0060.008
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.051
GPT teacher head0.348
Teacher spread0.296 · 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 designSystematic review
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

Citations27
Published2021
Admission routes1
Has abstractyes

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