Microalbuminuria in inflammatory bowel diseases using immunoturbidimetry and high-performance liquid chromatography.
Bibliographic record
Abstract
BACKGROUND AND STUDY AIMS: To measure urinary albumin excretion using immunoturbidimetry (IT) and high-performance liquid chromatography (HPLC) in inflammatory bowel diseases. PATIENTS AND METHODS: A cross-sectional study was carried out on 60 selected patients with Crohn's disease (CD), 57 with ulcerative colitis (UC) and 22 healthy volunteers, as controls. Urinary albumin excretion was measured by IT and HPLC, and albumin-creatinine ratio was calculated. This ratio was compared in patients with active and inactive CD and UC and in healthy volunteers. RESULTS: Patients with CD and UC had higher albumin-creatinine ratio compared to controls using both IT and HPLC (p < 0.05). We measured higher albumin-creatinine ratio in patients with active compared to inactive CD (p < 0.05). Albuminuria did not correlate with disease duration of CD or UC, but patients with more extended CD according to the Montreal classification had higher HPLC-albumin-creatinine ratio. In CD, we found a significant correlation between HPLC-albumin-creatinine ratio and some inflammatory markers i.e. white blood cells (p < 0.05) and erythrocyte sedimentation rate (p < 0.05). In UC, there was no significant correlation between HPLC-albumin-creatinine ratio and the above markers of systemic inflammation or activity of UC. Albumin-creatinine ratio measured by HPLC was higher in both active and inactive CD and UC groups than albumin-creatinine ratio measured by IT. Using a receiver operating characteristics curve analysis, in case of HPLC-albumin-creatinine ratio cut-off values of the activity of CD were 2.46 mg/mmol for men, 5.30 mg/mmol for women. CONCLUSIONS: HPLC-urinary albumin-creatinine ratio is associated with the clinical and laboratory disease activity indices in CD, but not in UC. Using HPLC we found a more sensitive method compared to IT to measure albuminuria that would be a sensitive activity marker in 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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".