Validation of the BETA-2 Score: An Improved Tool to Estimate Beta Cell Function After Clinical Islet Transplantation Using a Single Fasting Blood Sample
Bibliographic record
Abstract
The beta score, a composite measure of beta cell function after islet transplantation, has limited sensitivity because of its categorical nature and requires a mixed-meal tolerance test (MMTT). We developed a novel score based on a single fasting blood sample. The BETA-2 score used stepwise forward linear regression incorporating glucose (in millimoles per liter), C-peptide (in nanomoles per liter), hemoglobin A1c (as a percentage) and insulin dose (U/kg per day) as continuous variables from the original beta score data set (n = 183 MMTTs). Primary and secondary analyses assessed the score's ability to detect glucose intolerance (90-min MMTT glucose ≥8 mmol/L) and insulin independence, respectively. A validation cohort of islet transplant recipients (n = 114 MMTTs) examined 12 mo after transplantation was used to compare the score's ability to detect these outcomes. The BETA-2 score was expressed as follows (range 0-42): [Formula: see text] A score <20 and ≥15 detected glucose intolerance and insulin independence, respectively, with >82% sensitivity and specificity. The BETA-2 score demonstrated greater discrimination than the beta score for these outcomes (p < 0.05). Using a fasting blood sample, the BETA-2 score estimates graft function as a continuous variable and shows greater discrimination of glucose intolerance and insulin independence after transplantation versus the beta score, allowing frequent assessments of graft function. Studies examining its utility to track long-term graft function are required.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".