Anti-Saccharomyces cerevisiae Antibodies as a Prognostic Indicator in Crohnʼs Disease
Bibliographic record
Abstract
Purpose: Anti-Saccharomyces cerevisiae antibodies (ASCA) are relatively specific serological markers of Crohn's Disease (CD) and have been suggested as useful tools in diagnosis and prognostic stratification of patients. The purpose was to determine the association of ASCA with the phenotypic characteristics and prognosis of CD. Methods: We included 206 patients with CD, 95 men and 111 women for whom we evaluated the location and behavior of the disease and the need for corticosteroid therapy, immunosuppression, biological therapy, hospitalization and surgery. ASCA serology was determined by ELISA (IBDX® ELISA kits; GlyCochip®), considering seropositivity for values> 1/100. The statistical analysis was performed with SPSS 17.0 software (Chi2 test). Results: According to the Montreal classification, we identified seven patients A1 (3.4%), 127 A2 (61.7%) and 72 A3 (35.0%); 98 patients L1 (47.6%), 31 L2 (15.0%), 75 L3 (36.4%) and 23 L4 (11.2%); 22 patients B1 (59.2%), 43 B2 (20.9%) and 41 B3 (19.9%). Perianal disease was present in 58 patients (28.2%). One hundred and twelve patients (54.4%) required at least one cycle of steroids, 64 (31.1%) were treated with azathioprine and 28 (13.6%) with biological therapy. Eighty-one patients (39.3%) required hospitalization and 69 (33.5%) underwent surgery. ASCA were positive in 104 patients (50.5%). The seropositivity was associated statistically significantly with ileal disease (46% vs. 26%, p=0.01), fibrostenosing or penetrating behavior (28% vs. 13%, p <0.0001) and with the need for immunosuppression (19% vs. 12%, p=0.044), hospitalization (24% vs. 16%, p=0.021) and surgery (22% vs. 11%, p=0.001). Conclusion: According to our series ASCA positive patients have a more frequent ileal involvement, a greater need for immunosuppression and a worse course of disease. These could be considered when making treatment decisions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".