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Record W1483146788 · doi:10.3346/jkms.2015.30.7.991

Reversal of Hypoglycemia Unawareness with a Single-donor, Marginal Dose Allogeneic Islet Transplantation in Korea: A Case Report

2015· article· en· W1483146788 on OpenAlexaboutno aff
Hae Kyung Yang, Dong‐Sik Ham, Heon‐Seok Park, Marie Rhee, Young‐Hye You, Min Jung Kim, Ji-Won Kim, Seung‐Hwan Lee, Tae Ho Hong, Byung Gil Choi, Jae‐Hyoung Cho, Kun‐Ho Yoon

Bibliographic record

VenueJournal of Korean Medical Science · 2015
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsnot available
FundersCatholic Institute of Cell TherapyMinistry of Health and Welfare
KeywordsMedicineIsletTransplantationHypoglycemiaDiabetes mellitusType 1 diabetesInsulinLeukopeniaRegimenInternal medicineGlycemicGastroenterologyEndocrinologyChemotherapy

Abstract

fetched live from OpenAlex

Pancreatic islet transplantation is a physiologically advantageous and minimally invasive procedure for the treatment of type 1 diabetes mellitus. Here, we describe the first reported case of successful allogeneic islet transplantation alone, using single-donor, marginal-dose islets in a Korean patient. A 59-yr-old patient with type 1 diabetes mellitus, who suffered from recurrent severe hypoglycemia, received 4,163 islet equivalents/kg from a single brain-death donor. Isolated islets were infused intraportally without any complications. The immunosuppressive regimen was based on the Edmonton protocol, but the maintenance dosage was reduced because of mucositis and leukopenia. Although insulin independence was not achieved, the patient showed stabilized blood glucose concentration, reduced insulin dosage and reversal of hypoglycemic unawareness, even with marginal dose of islets and reduced immunosuppressant. Islet transplantation may successfully improve endogenous insulin production and glycemic stability in subjects with type 1 diabetes mellitus.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score0.507

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.297
Teacher spread0.262 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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".

Quick stats

Citations5
Published2015
Admission routes1
Has abstractyes

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