Unexpected random urinary protein:creatinine ratio results–limitations of the pyrocatechol violet-dye method
Bibliographic record
Abstract
BACKGROUND: For clinicians, it is important to rely on accurate laboratory results for patient care and optimal use of health care resources. We sought to explore our observations that urine protein:creatinine ratios (PrCr) ≥30 mg/mmol are seen not infrequently associated with normal pregnancy outcome. METHODS: Urine samples were collected prospectively from 160 pregnant women attending high-risk maternity clinics at a tertiary care facility. Urinary protein was measured using a pyrocatechol violet assay and urinary creatinine by an enzymatic method on Vitros analysers. Maternal/perinatal outcomes were abstracted from hospital records. RESULTS: 91/233 (39.1%) samples had a PrCr ≥30 mg/mmol, especially when urinary creatinine concentration was <3 mM (94.1%) vs. ≥3 mM (16.4%) (p < 0.001). When using the last sample before delivery, 47/160 (29.4%) had a PrCr ≥30 mg/mmol in diluted urine vs. only 17/160 (15.4%) in more concentrated urine (p < 0.001); PrCr positive results were also more frequent among the 32 (20.0%) women with known normal pregnancy outcome (90.9% vs. 0) (p < 0.001). Using the same analyser, 0.12 g/L urinary protein was 'detected' in deionised water. Re-analysis of data from two cohorts revealed substantially less inflation of PrCr in dilute urine using a pyrogallol red assay. CONCLUSIONS: Random urinary PrCr was overestimated in dilute urine when tested using a common pyrocatechol violet dye-based method. This effect was reduced in cohorts when pyrogallol red assays were used. False positive results can impact on diagnosis and patient care. This highlights the need for both clinical and laboratory quality improvement projects and standardization of laboratory protein measurement.
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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 | 0.082 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 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".