Continuous Glucose Monitoring and Long-Term Assessment of Islet Function in Autologous Islet Transplantation after Total Pancreatectomy for Neoplasm: Preliminary Insights from a Prospective Study
Bibliographic record
Abstract
Background and Aims: Total pancreatectomy with islet autotransplantation (TPIAT) is a surgical option to mitigate the risk of anastomotic complications and preserve endogenous insulin secretion in patients undergoing pancreaticoduodenectomy. However, the utility of continuous glucose monitoring (CGM) in assessing islet graft performance remains poorly characterized. Thereby, the aim of this study was to investigate the relationship between CGM-derived glycemic metrics and islet function following TPIAT. Materials and Methods: Ten patients with pancreatic neoplasms (male/female 5/5, median age 60 [IQR 55–68] years) underwent TPIAT between September 2023 and March 2025 at the Verona University Hospital, receiving a median islet dose of 1912 IEQ/kg [IQR 1724–3074]. CGM data were collected at 3 ( n = 10), 6 ( n = 8), and 12 ( n = 7) months post-transplantation. Islet metabolic function was assessed using Igls criteria and BETA-2 score. CGM metrics were compared across Igls-defined graft function categories and correlated with BETA-2 scores. Results: Of 25 total assessments, islet function was classified as optimal ( n = 10), good ( n = 6), marginal ( n = 8), or failure ( n = 1). Median BETA-2 score decreased significantly across these groups (19.4, 13.6, 5.3, 1.4, respectively; P < 0.001). Optimal function was associated with superior glycemic control (time in range, TIR: 97.0%; time in tight range, TITR: 86.5%; time above range, TAR: 1.5%) and lower glycemic variability (coefficient of variation, CV: 20.5%; glycemia risk index, GRI: 44.0), compared with good and marginal groups (all P < 0.01). These same CGM metrics were significantly correlated with both Igls classification and BETA-2 score (all P < 0.015). Conclusions: CGM parameters reflect islet graft performance following TPIAT and are strongly correlated with established markers of β-cell function. Metrics such as TIR, TITR, TAR, CV, and GRI may serve as practical and sensitive tools for post-transplant metabolic surveillance in endocrine clinical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".