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Record W2584529311

SPECIFIC CARBOHYDRATE DIET FOR INFLAMMATORY BOWEL DISEASE

2016· article· en· W2584529311 on OpenAlexaff
Jennifer Pranger

Bibliographic record

VenuePrinciples of Security and Trust · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsLakehead University
Fundersnot available
KeywordsInflammatory bowel diseaseMedicineDiseasePopulationCrohn's diseaseMedical recordInternal medicineEnvironmental health
DOInot available

Abstract

fetched live from OpenAlex

Dietary treatments have been looked at as a method of controlling and reducing the number of exacerbations associated with inflammatory bowel disease. This method of treatment is relatively new and under-researched but is hypothesized to induce drug-free remissions. This meta-analysis compares two exploratory survey-style studies that look at the effect of the Specific Carbohydrate Diet (SCD) on individuals with Inflammatory Bowel Disease (IBD). Kakodkar, Farooqui, Mikolaitis, & Mutlu's 2015 article, Specific Carbohydrate Diet for Inflammatory Bowel Disease: A Case Series obtained a sample size of 50 participants using convenience sampling. Medical records, a three-day diet diary, a structured survey of their medical history and a validated disease activity index were used to collect data from this population. Suskind, Wahbeh, Gregory, Vendettuoli & Christie's 2014 article, ''Nutritional Therapy in Pediatric Crohn Disease'' used convenience sampling and had a sample size of seven. All participants had Inflammatory Bowel Disease and were using the SCD to treat it. The second study further narrowed its criteria by restricting the population to pediatric patients. A retrospective chart review was used to study this population. Both studies found that the SCD could be effective in managing IBD, but due to their limitations and in order to provide conclusive evidence, it is clear that more research needs to be done.

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 score0.297
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.011
GPT teacher head0.223
Teacher spread0.212 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

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