The Urinary Protein-to-Creatinine Ratio in Canadian Women at Risk of Preeclampsia: Does the Time of Day of Testing Matter?
Bibliographic record
Abstract
OBJECTIVES: To determine the performance of a protein-to-creatinine ratio threshold of 30 mg/mmol in pregnant women investigated for hypertension according to the time of day of the sample. METHODS: This prospective study included ambulatory pregnant women investigated for hypertensive disorders. A single voided random urine specimen was obtained to determine the protein-to-creatinine ratio, followed immediately by a 24-hour urine collection. Statistical analyses included Spearman correlation, sensitivity, specificity, predictive values, likelihood ratios, and receiver-operator characteristic curves with 95% confidence intervals. A P value < 0.05 was considered statistically significant. RESULTS: Among the 91 specimens analyzed, 47.3% showed significant proteinuria in the 24-hour collection and 33% were first morning samples. The protein-to-creatinine ratio and 24-hour urinary protein excretion were highly correlated (r = 0.92, P < 0.001). The diagnostic accuracy of the protein-to-creatinine ratio threshold of 30 mg/mmol was lower in first morning samples than in samples obtained during the rest of the day, with sensitivity 58% and 90%, specificity 93% and 100%, positive predictive value 88% and 100%, negative predictive value 72% and 92%, positive likelihood ratio 8 and not calculable, and negative likelihood ratio 0.45 and 0.1, respectively. The receiver-operator characteristic area under the curve was 0.94 (95% CI 0.86 to 1) for first morning samples and 1.0 (95% CI 0.99 to 1) for other samples. CONCLUSION: A protein-to-creatinine ratio threshold of 30 mg/mmol reliably identifies significant proteinuria, but its reliability is reduced in first morning samples. Consequently, such samples should not be used for this purpose.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".