Role of urinary potassium in ulcerative colitis and its correlation with disease expression: A cross-sectional study
Bibliographic record
Abstract
ABSTRACT Background: Environmental factors play an important role in regulating the balance between inflammation and immune tolerance in inflammatory bowel diseases. Potassium may be associated with an anti-inflammatory response. There is limited literature addressing the relationship between potassium and gut inflammatory responses. Hence, this study aims to assess the role of urinary potassium as a marker for ulcerative colitis (UC) disease activity. Methods: A cross-sectional study was conducted on 20 patients with active UC, 20 patients with UC in remission, and 20 subjects as a control group. Clinical and endoscopic disease activity were determined by the Mayo score. A 24-h urinary potassium level was measured, and analysis was performed using an ion-selective electrode Cobas 6000 (Roche) system. Results: Urinary potassium was significantly lower in patients with active UC compared to those with inactive UC and the control group (mean 21.82 ± 26.24 mEq/day vs. mean 46.35 ± 21.42 mEq/day and mean 42.18 ± 5.31 mEq/day, respectively, P < 0.001). Urinary potassium correlated negatively with Mayo score ( P = 0.002), Montreal classification ( P = 0.001), fecal calprotectin ( P < 0.001), and C-reactive protein ( P = 0.001). Receiver-operating characteristic curve showed that urinary potassium can significantly discriminate between active and inactive UC at a cutoff level ≤28.2 mEq/day with 85.0% sensitivity, 75.0% specificity, 77.3% positive predictive value, and 83.3% negative predictive value with 80.0% overall accuracy. Conclusion: A 24-h urinary potassium measurement at a cutoff of ≤28.2 mEq/day could be an effective complementary marker for diagnosing active UC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".