Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".