P551 Platelet-to-lymphocyte ratio index for non-invasive assessment of endoscopic activity in small bowel Crohn’s disease: application and prospective validation.
Bibliographic record
Abstract
Abstract Background The platelet-to-lymphocyte ratio (PLR) index has recently been a focus of investigation as a reliable marker of inflammation, being shown to have a good accuracy upon predicting endoscopic remission in patients with colonic Crohn’s Disease (CD). We aimed to evaluate and validate the discriminative power of PLR index in patients with small bowel CD. Methods Single center study including patients with isolated small bowel CD (L1 ± L4 disease according to Montreal classification) undergoing small bowel capsule endoscopy (SBCE) for assessment of endoscopic activity. CD endoscopic activity was classified according to the Lewis score (LS) value. Complete blood count, C-reactive protein and fecal calprotectin values were collected within 1 month of SBCE. A retrospective sample was used for initial assessment of PLR index performance, followed by a prospective 2-years application on a distinct sample. Results The initial sample included 49 and the validation sample 48 patients, both groups being age- and gender-matched. On the initial cohort, PLR index presented a positive moderate correlation with LS (k=0.597; p<0.001), which was stronger than the one found with fecal calprotectin (k=0.525; p=0.001) or C-reactive protein (k=0.321; p=0.029). PLR index presented an excellent accuracy for predicting patients with a moderate-to-severe endoscopic activity (AUC=0.91; 95%CI=0.82-0.99; p<0.001), and a good accuracy for prediction of mucosal healing (AUC=0.74; 95%CI=0.60-0.89; p=0.007). These results were confirmed on the prospective validation cohort, as the correlation of LS with PLR index (k=0.631; p<0.001) was further stronger than with fecal calprotectin (k=0.355; p=0.040) and C-reactive protein (k=0.183; p=0.219). The accuracy of PLR index was confirmed to be excellent for moderate-to-severe endoscopic activity (AUC=0.87; 95%CI=0.76-0.98; p<0.001) and good for mucosal healing (AUC=0.74; 95%CI=0.59-0.87; p<0.001). Conclusion PLR index demonstrated an excellent acuity in predicting moderate-to-severe disease and good acuity in predicting mucosal healing in patients with small bowel CD, with both associations confirmed on a prospective validation cohort. Our findings establish this index as a promising and easy-to-apply tool for non-invasive and regular follow-up of patients with small bowel CD.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".