Dosagem do peptídeo C sérico ao acaso em adultos com diagnóstico clínico de diabetes mellitus tipo 1
Bibliographic record
Abstract
OBJECTIVE: C peptide measurement can be helpful for classification of diabetes mellitus (DM). The aim of this study was to investigate the association between clinical diagnosis of type 1 diabetes (T1D) and levels of random C peptide. METHODS: Random C peptide was measured in adults of multi-ethnic background who had been classified as having T1D according to their clinical presentation. All individuals were > 18 years old at onset. RESULTS: The study included 51 adults, 28 (54.9%) females and 23 (45.1%) males, 36 (70.6%) Caucasian and 15 (29.4%) non-Caucasian. Their mean age at onset and duration of DM mean age were 27.9 (+/- 7.5) years and 9.9 (+/-7.2) years, respectively. In 8 patients (15.7%) C peptide was > 1.5 ng/ml, indicating sustained beta cell function. In this group a higher level of body mass index (26.05 vs 23.05 kg/m(2); p=0.006) and a greater proportion of non Caucasian individuals (62.5% vs 23.3%; p=0.039)) were detected. CONCLUSION: Most patients with DM clinically classified as T1D exhibit low C peptide. However, pancreatic insulin secretion seems to be preserved in a significant proportion of those individuals, possibly representing an atypical form of DM, not yet elucidated, that combines characteristics of both T1D and T2D.
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| 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".