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 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 | 0.002 | 0.004 |
| 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 | 0.001 |
| 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".