β-Score
Bibliographic record
Abstract
OBJECTIVE: Success after islet transplantation can be defined in terms of insulin independence, C-peptide secretion, or glycemic control. These measures are interdependent and all need to be considered in evaluating beta-cell function after islet transplantation. For the current study, a composite beta-score was developed that provides an integrated measure of beta-cell function success after islet transplantation. RESEARCH DESIGN AND METHODS: The proposed scoring system gave 2 points each for normal fasting glucose, HbA(1c), stimulated C-peptide, and absence of insulin or oral hypoglycemic agent use. No points were awarded if the fasting glucose was in the diabetic range, the HbA(1c) was >6.9%, C-peptide secretion was absent on stimulation, or daily insulin use was in excess of 0.24 units/kg. One point was given for intermediate values. The score ranged from 0 to 8 and was correlated with the glucose value 90 min after a standard mixed meal challenge (n = 218) in 57 subjects before and after islet transplantation. The score was also used to follow subjects for up to 5 years after islet transplantation. RESULTS: The beta-score correlated well with the plasma glucose level 90 min after a mixed meal challenge (r = -0.849, P < 0.001). On follow-up, the beta-score rose after the first transplant and was maintained up to 5 years, demonstrating continuing function of the transplanted beta-cells. CONCLUSIONS: The beta-score provides a simple clinical scoring system that encompasses glycemic control, diabetes therapy, and endogenous insulin secretion that correlates well with physiological measures of beta-cell function. On this basis, it is suitable as an overall measure of beta-cell transplant function. The beta-score gives an integrated measure of beta-cell function as a continuum that may be more useful than simply assessing the presence or absence of insulin independence.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.074 | 0.033 |
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".