The TNF-α -308 polymorphism may affect the severity of Crohn's disease
Bibliographic record
Abstract
OBJECTIVE: The goal of this project was to analyze the association between Crohn's disease, its clinical features, and the tumor necrosis factor alpha (TNF-α) -308 polymorphism. METHODS: This is a case-control and cross-sectional study that enrolled 91 patients with Crohn's disease and 91 controls. Patients with Crohn's disease were characterized according to the Montreal Classification, along with their clinical and surgical treatment history. Analysis of the TNF-α -308 polymorphism was performed using a commercial kit. A stratified analysis was applied using an OR (odds ratio) with a 95% confidence interval. The chi-square and Fisher's exact tests were utilized for analysis of the association between the polymorphism and the clinical features of Crohn's disease. RESULTS: The low producer predicted phenotype was present in 76.9% of Crohn's disease cases and 75.8% of controls (OR 0.94 [0.45-1.97]). The TNF2 allele and the high producer predicted phenotype were more frequent among patients with Crohn's disease penetrating behavior (p = 0.004). The TNF2 allele and the high producer predicted phenotype were also associated with a history of colectomy (p = 0.02), and the TNF2 allele was associated with small bowel resection (p = 0.03). CONCLUSIONS: The TNF-α -308 polymorphism appears to affect the severity of the disease. However, TNF-α -308 polymorphism does not appear to be important for the susceptibility in the development of Crohn's 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".