C-peptide Targets and Patient-centered Outcomes of Relevance to Cellular Transplantation for Diabetes
Bibliographic record
Abstract
BACKGROUND: C-peptide levels are a key measure of beta-cell mass following islet transplantation, but threshold values required to achieve clinically relevant patient-centered outcomes are not yet established. METHODS: We conducted a cross-sectional retrospective cohort study evaluating patients undergoing islet transplantation at a single center from 1999 to 2018. Cohorts included patients achieving insulin independence without hypoglycemia, those with insulin dependence without hypoglycemia, and those with recurrent symptomatic hypoglycemia. Primary outcome was fasting C-peptide levels at 6 to 12 mo postfirst transplant; secondary outcomes included stimulated C-peptide levels and BETA-2 scores. Fasting and stimulated C-peptide and BETA-2 cutoff values for determination of hypoglycemic freedom and insulin independence were evaluated using receiver operating characteristic curves. RESULTS: We analyzed 192 patients, with 122 (63.5%) being insulin independent without hypoglycemia, 61 (31.8%) being insulin dependent without hypoglycemia, and 9 (4.7%) experiencing recurrent symptomatic hypoglycemia. Patients with insulin independence had a median (interquartile range) fasting C-peptide level of 0.66 nmol/L (0.34 nmol/L), compared with 0.49 nmol/L (0.25 nmol/L) for those being insulin dependent without hypoglycemia and 0.07 nmol/L (0.05 nmol/L) for patients experiencing hypoglycemia ( P < 0.001). Optimal fasting C-peptide cutoffs for insulin independence and hypoglycemia were ≥0.50 nmol/L and ≥0.12 nmol/L, respectively. Cutoffs for insulin independence and freedom of hypoglycemia using stimulated C-peptide were ≥1.2 nmol/L and ≥0.68 nmol/L, respectively, whereas optimal cutoff BETA-2 scores were ≥16.4 and ≥5.2. CONCLUSIONS: We define C-peptide levels and BETA-2 scores associated with patient-centered outcomes. Characterizing these values will enable evaluation of ongoing clinical trials with islet or stem cell therapies.
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".