Bibliographic record
Abstract
Diabetic polyneuropathy (DPN) is the most common chronic complication of diabetes and affects Type 1 diabetic patients disproportionately. In the last two decades it has become increasingly evident that underlying metabolic, molecular and functional mechanisms and, ultimately, structural changes differ in DPN between the two major types of diabetes. In Type 1 diabetes, impaired insulin/C-peptide action has emerged as a prominent pathogenetic factor. C-peptide was long considered to be biologically inactive. During the last number of years it has been shown to have a number of insulin-like effects but without affecting blood glucose levels. Preclinical studies have demonstrated effects on Na(+)/K(+)-ATPase activity, endothelial nitric oxide synthase, expression of neurotrophic factors and regulation of molecular species underlying the degeneration of the nodal apparatus in Type 1 diabetic nerves, as well as DNA binding of transcription factors and modulation of apoptotic phenomena. In animal studies, these effects have translated into protection and improvement of functional abnormalities, promotion of nerve fibre regeneration, protection of structural changes and amelioration of apoptotic phenomena targeting central and peripheral nerve cell constituents. Several small-scale clinical trials confirm these beneficial effects on autonomic and somatic nerve function and blood flow in a variety of tissues. Therefore, evidence to date indicating that replacement of C-peptide in patients with Type 1 diabetes will retard and prevent chronic complication is real and encouraging. Large-scale clinical trials necessary to bring this natural substance into the clinical arena should, therefore, be encouraged and accelerated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".