Fecal Leukocyte Esterase, an Alternative Biomarker to Fecal Calprotectin in Inflammatory Bowel Disease: A Pilot Series
Bibliographic record
Abstract
Background and Aims: Fecal calprotectin (FC) is a noninvasive biomarker used in inflammatory bowel disease (IBD) management and risk stratification of nonspecific gastrointestinal symptoms. Leukocyte esterase is an inexpensive and widely available point-of-care inflammatory marker present on urinalysis test strips. We aim to assess the diagnostic accuracy of fecal leukocyte esterase (FLE) relative to FC and endoscopy and demonstrate its use as an alternative biomarker for IBD. Methods: In this prospective cohort study, 70 patients who had FC ordered as part of standard clinical care also received FLE testing. FLE levels were compared with various FC cutoff values and endoscopy and pathology findings as the gold standard. Results: As the FC cutoff increased from 50 to 500 μg/g, FLE sensitivity increased from 67% to 95% while the specificity decreased from 86% to 76%. The area under the receiver operating characteristic (AUROC) curve increased from 0.79 to 0.90. An FLE of ≥1+ had the best test characteristics. Among patients who underwent endoscopic evaluation, FLE demonstrated an identical sensitivity (75%) and specificity (86%) to FC in predicting endoscopic inflammation. AUROC was 0.80 for FLE and 0.85 for FC with an optimal cutoff of ≥2+ and 301 μg/g, respectively. When used to distinguish between patients with active IBD and no/inactive IBD, FLE had a sensitivity of 84% and specificity of 90%, comparable with the 84% and 83%, respectively, of FC. AUROC was 0.88 for FLE and 0.91 for FC with an optimal cutoff of ≥2+ and 145 μg/g, respectively. Conclusion: FLE demonstrates adequate correlation and comparable accuracy with FC in predicting endoscopic inflammation and distinguishing between patients with active vs inactive IBD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".