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Record W2023970069 · doi:10.1586/1744666x.1.2.277

Complementary and alternative medicine in inflammatory bowel disease: keeping an open mind

2005· article· en· W2023970069 on OpenAlexaff
Jennifer Durber, Anthony Otley

Bibliographic record

VenueExpert Review of Clinical Immunology · 2005
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsIzaak Walton Killam Health Centre
Fundersnot available
KeywordsMedicineAlternative medicineInflammatory bowel diseaseHomeopathyIntensive care medicineClinical trialDiseasePopulationInflammatory Bowel DiseasesFamily medicineTraditional medicineInternal medicinePathology

Abstract

fetched live from OpenAlex

Complementary and alternative medicine use is increasing in both the general population and in individuals with chronic illness. A significant proportion of adult and pediatric patients with inflammatory bowel disease use or have used complementary and alternative medicine as determined by multiple surveys conducted in North America, Europe and Australia. There was a heterogenous selection of complementary and alternative medicine therapies chosen by patients, with variations due to geography and age. However, in general, their use is sought due to frustration with conventional therapies and the perceived safety of complementary and alternative medicine therapies. One of the dilemmas that arises when assessing the topic of complementary and alternative medicine use is rapidly changing definitions of what is and what is not considered conventional medicine. With our increasing understanding of the intricacies of the gastrointestinal immune system and host-microflora interactions, determining a scientific basis behind many complementary and alternative medicine therapies in vitro or in vivo is leading to evaluation of these therapies in humans. This will require well-designed and rigourously conducted clinical trials with sufficient sample size.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.724
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.144
GPT teacher head0.515
Teacher spread0.371 · 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 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

Citations4
Published2005
Admission routes1
Has abstractyes

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