Stimulated urine C‐peptide creatinine ratio vs serum C‐peptide level for monitoring of β‐cell function in the first year after diagnosis of Type 1 diabetes
Bibliographic record
Abstract
Abstract Aims To determine if urine C‐peptide/creatinine ratio is a useful tool for monitoring β‐cell function in new‐onset Type 1 diabetes. Methods Data were obtained from a prospective immunomodulation study in people with Type 1 diabetes ≤ 3 months from diagnosis, with a standard mixed‐meal tolerance test and measurement of urine C‐peptide/creatinine ratio carried out at 0, 3, 6, 9 and 12 months. The change in the insulin‐dose‐adjusted HbA1c level was also correlated with the change in serum/urine C‐peptide level during the 12‐month follow‐up period. Results A significant reduction in urine C‐peptide/creatinine ratio, measured after a mixed‐meal, was reached at 9 months (−45.4%), whilst the reduction in stimulated serum C‐peptide level reached significance after 3 months (−54.7%) in placebo‐treated participants. Neither change in stimulated serum C‐peptide nor change in urine C‐peptide level correlated with each other, and nor did change in insulin‐dose‐adjusted HbA1c level in the first 6 months, but all measures correlated significantly in the second half of the 12‐month follow‐up period. Conclusion Mixed‐meal‐stimulated urine C‐peptide/creatinine ratio was similar to, although less sensitive than, stimulated serum C‐peptide level in monitoring β‐cell function during the first year after diagnosis. Because the former is significantly less invasive, it warrants inclusion in further studies in Type 1 diabetes and may represent an attractive alternative outcome measure in cohort studies and in children.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".