Long-term Prognosis of Nonceliac Enteropathies and a Score to Identify Patients With Poor Outcomes: A 30-year Multicenter Longitudinal Study
Bibliographic record
Abstract
INTRODUCTION: Long-term prognosis of nonceliac enteropathies (NCEs) is poorly understood. We aimed to evaluate long-term outcomes and develop a prognostic score for NCEs. METHODS: NCEs patients from an international multicenter cohort (4 Italian centers, 1 United Kingdom, 1 French, 1 Norwegian, 1 United States, 1 Indian) followed-up over 30 years were enrolled. Complications and mortality were analyzed with Kaplan-Meier curves, standardized mortality ratios (SMR), and multivariate Cox regression. A clinical score to identify patients at risk of poor outcomes was developed. RESULTS: Two hundred sixty-one patients were enrolled (144 female, mean age at diagnosis 49 ± 18 years, median follow-up 70 months, interquartile range 24-109). The most common etiologies were idiopathic villous atrophy (39%), drug related (17%), common variable immune deficiency (15%), infectious (10%), and autoimmune enteropathy (9%). Five-year and 10-year complication-free survival were 89% and 77%, respectively, whereas 5-year and 10-year overall survival were 88% and 74%, respectively. Causes of death included sepsis/major infections (22%), lymphoproliferative disorders (22%), solid-organ malignancies (12%), cardiovascular/metabolic disease (10%), and was unknown in 33%. Mortality was increased in NCEs compared with the general population (SMR 3.17, 95% confidence interval [CI] 2.24-4.34). Older age at diagnosis ( P < 0.001), anemia (hazard ratio [HR] 2.53, 95% CI 1.33-4.80, P < 0.01), and lack of clinical (HR 3.21, 95% CI 1.68-6.18, P < 0.01) and histological response (HR 2.14, 95% CI 1.08-4.23, P = 0.04) were independent predictors of mortality at Cox regression. A 5-point score was developed to identify high-risk patients: very low risk (0 pts), low risk (1-2 pts), intermediate risk (3 pts), and high risk (4-5 pts), with 10-year survival rates of 100%, 87%, 62%, and 16%, respectively. DISCUSSION: Mortality in NCEs is increased because of complications and lack of response to current therapies. We developed a clinical score to personalize follow-up. Targeted treatments are needed to improve outcomes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".