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Record W4414619526 · doi:10.1177/15209156251380857

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

2025· article· en· W4414619526 on OpenAlexaff
Alessandro Csermely, Massimiliano Tuveri, Gabriella Lionetto, Martina Fontana, Giancarlo Mansueto, Anna Turazzini, Sara S. Sheiban, Federica Nocilla, Raffaella Melzi, Rita Nano, Alessandro Mantovani, Riccardo C. Bonadonna, Maddalena Trombetta, Lorenzo Piemonti, Roberto Salvia

Bibliographic record

VenueDiabetes Technology & Therapeutics · 2025
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsPancreas Centre (Canada)
Fundersnot available
KeywordsIsletContinuous glucose monitoringGlycemicAutotransplantationProspective cohort studyDiabetes mellitusTransplantation

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.277
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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