Utility of Serological Markers in Predicting the Early Occurrence of Complications and Surgery in Pediatric Crohn's Disease Patients
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Many Crohn's disease (CD) patients develop complications (fistulae and abscesses), and require surgery, often repeatedly and at variable instances. Identifying serological markers that determine their early or repeated manifestation can enable implementing more aggressive preventive strategies. Our objective was to study the ability of serological markers for predicting development of early (first) and recurrent complications or requirement for surgery. METHODS: Serum anti-Saccharomyces cervisiae (ASCA) (IgA & IgG) and perinuclear antineutrophil cytoplasmic antibodies (pANCA) were assayed close to diagnosis in a pediatric cohort of CD patients identified between 1996 and 1998. At diagnosis and follow-up, information was acquired on demographic and clinical features of disease. Relation between ASCA and clinical events was studied using adjusted Cox-proportional hazards modeling. The relative rates of recurrent clinical events according to the marker measures were compared. RESULTS: The mean age (SD) at diagnosis was 11.2 (3.4) yr. Among 139 patients, 35 (25.9%) and 31 (22.3%) acquired one or more CD related surgery or complication, respectively. Time to occurrence of the first complication was lower among patients ASCA+ (IgA or IgG) (hazards ratio (HR) = 2.33; 95% confidence interval (CI) = 0.99-5.50) and among those with higher ASCA-IgA titers (HR = 1.20; 95% CI = 1.08-1.34). The rates of recurrent complications were higher among those positive or with higher ASCA titers. ASCA did not predict time to undergoing surgery independent of complications, and was unrelated to the occurrence of recurrent surgeries. CONCLUSIONS: Our study shows that serum ASCA measured close to diagnosis can determine the occurrence of early complications in pediatric CD. Preventive treatment targeted toward these susceptible patients could potentially modify the disease course.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".