Bibliographic record
Abstract
Introduction. A positive family history is the strongest known risk factor for the development of inflammatory bowel disease. Epidemiological data from familial and twin studies suggest genetic factors play an important role in the onset and course of inflammatory bowel disease. The objective of these studies is to evaluate the contribution of several biologically relevant candidate genes to phenotypic manifestations of inflammatory bowel disease. In addition, a study was conducted to assess the contribution of diagnostic misclassification on the ability to identify inflammatory bowel disease susceptibility loci. Methods. The first three studies utilized well characterized individuals with inflammatory bowel disease and selected polymorphisms in candidate genes to perform case-control association studies in order to ascertain the contribution of such polymorphisms to the phenotype of interest. The final study involved an assessment of the rate of diagnostic misclassification in an inflammatory bowel disease genetics study and a determination as to the impact of such misclassification on the ability to identify inflammatory bowel disease susceptibility loci. Results. Positive associations between alleles of selected human leukocyte antigen genes and the diagnosis of Crohn's disease, ulcerative colitis, colonic inflammatory bowel disease and diminished bone mineral density in inflammatory bowel disease patients were identified. An important allele implicated in susceptibility to sporadic colorectal neoplasia in selected populations was deemed not to have a role in the susceptibility to inflammatory bowel disease-associated colorectal cancer. Relatively low diagnostic misclassification rates were identified in patients enrolled in an inflammatory bowel disease genetics study, however, even these low rates resulted in a substantial loss of power to detect to detect true susceptibility loci. Conclusions. The identification of alleles associated with phenotypic characteristics of patients with inflammatory bowel disease will assist in attempts to identify those at risk for specific clinical features, in reducing clinical and genetic heterogeneity in inflammatory bowel disease assessment and provide information for further research into mechanisms of the occurrence of such phenotypes. Careful documentation of disease diagnosis and phenotypic manifestations is paramount in aiding the discovery of important susceptibility genes in inflammatory bowel disease.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".