Variables to Predict Nephrological Disease in General, and Glomerulonephritis in Particular, in Patients With Microhematuria
Bibliographic record
Abstract
BACKGROUND: Microhematuria (MH) is a symptom frequently leading to uncertainty as to when a nephrology referral is appropriate. Because MH may be indicative of severe kidney disorders, prompt diagnosis and potential treatment initiation can be important. We aimed to identify further variables that point at a nephrological cause, in particular of glomerulonephritis (GN), when MH is diagnosed. METHODS: A retrospective analysis of data acquired from patients attending a nephrology office due to MH was performed. Demographic information and diagnostic tests were evaluated in order to identify factors that were associated with a nephrological cause. RESULTS: Patients with MH (n = 805) as indicated by a urine stick analysis were included. Of these, MH was confirmed by urine sediment analysis in 543 patients (67.5%). Of those, 48.3% had a nephrological cause, including 12.4% with GN and 2.9% with rapid progressive GN (RPGN). A urine dipstick finding of ≥ 250 erythrocytes per microliter, microalbuminuria and elevated leukocytes increased the probability of having a GN to 62.4%. Furthermore, the presence of microalbuminuria, GFR < 60 mL/min, history of hypertension and diabetes mellitus increased the probability for all nephrological causes to 95.4%. CONCLUSION: There are a number of factors available that help to assess the need for a nephrology referral in patients with microhematuria.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".