Leucine-rich alpha-2 glycoprotein is useful in predicting clinical relapse in patients with Crohn’s disease during biological remission
Bibliographic record
Abstract
BACKGROUND/AIMS: Serum leucine-rich alpha-2 glycoprotein (LRG) is a potential biomarker of Crohn's disease (CD). This study aimed to evaluate the usefulness of LRG in predicting clinical relapse in patients in remission with CD. METHODS: This retrospective observational study assessed the relationships among patient-reported outcome (PRO2), LRG, and other blood markers. The influence of LRG on clinical relapse was assessed in patients in remission with CD. RESULTS: Data of 94 patients tested for LRG between January 2021 and May 2023 were collected. LRG level did not correlate with PRO2 score (ρ = 0.06); however, it strongly correlated with C-reactive protein (CRP) level (r=0.79) and serum albumin level (r=-0.70). Among 69 patients in clinical remission, relapse occurred in 22 patients (31.9%). In the context of predicting relapse, LRG showed the highest area under the curve, followed by CRP level, platelet count, and albumin level. Multivariate analysis revealed that only LRG (P= 0.02) was an independent factor for predicting clinical remission. The cumulative non-relapse rate was significantly higher in patients with LRG < 13.8 μg/mL than in patients in remission with LRG ≥ 13.8 μg/mL and normal CRP level (P= 0.002) or normal albumin level (P= 0.001). Cumulative non-relapse rate was also higher in patients with LRG < 13.8 μg/mL compared to those with LRG ≥ 13.8 μg/mL in patients with L3 or B2+B3 of Montreal calcification. CONCLUSIONS: LRG is useful in predicting clinical relapse in patients with CD during biological remission. LRG is a useful biomarker for predicting prognosis, even in patients with intestinal stenosis, or previous/present fistulas.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".