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Record W3031965734 · doi:10.1177/1756284820927550

Complementary and alternative medications in the management of inflammatory bowel disease

2020· review· en· W3031965734 on OpenAlexaff
Sherman Picardo, Mansour Altuwaijri, Shane Devlin, Cynthia H. Seow

Bibliographic record

VenueTherapeutic Advances in Gastroenterology · 2020
Typereview
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineInflammatory bowel diseaseIntensive care medicineDiseaseNarrative reviewCrohn's diseaseAlternative medicineUlcerative colitisDrugInternal medicinePharmacologyPathology

Abstract

fetched live from OpenAlex

The use of complementary and alternative medications (CAM), products, and therapies not considered to be part of conventional medicine is common among patients with inflammatory bowel disease (IBD). Patients often turn to these therapies as they are considered natural and safe, with significant benefit reported beyond disease control. There is emerging evidence that some of these therapies may have anti-inflammatory activity; however, robust evidence for their efficacy in modulating disease activity is currently lacking. Patients often avoid discussing the use of CAM with their physicians, which may lead to drug interactions and/or reduced adherence with conventional therapy. It is important for physicians to be aware of the commonly used CAM and current evidence behind these therapies in order to better counsel their patients about their use in the management of IBD. This narrative review provides an overview of the evidence of the more commonly used CAM in patients with IBD.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.046
GPT teacher head0.387
Teacher spread0.341 · 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

Citations33
Published2020
Admission routes1
Has abstractyes

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