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Record W2020922698 · doi:10.1159/000053313

Islet Cell Transplantation and Other New Technologies for Treating Type 1 Diabetes: A Paediatric View

2002· review· en· W2020922698 on OpenAlexaff
Denis Daneman

Bibliographic record

VenueHormone Research in Paediatrics · 2002
Typereview
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsSickKids FoundationHospital for Sick Children
Fundersnot available
KeywordsMedicineIntensive care medicineIsletTransplantationType 1 diabetesPancreas transplantationOptimismDiabetes mellitusArtificial pancreasType 2 diabetesIslet cell transplantationInsulinMetabolic control analysisPancreasInternal medicineEndocrinologyPsychologyKidney transplantation

Abstract

fetched live from OpenAlex

Recent advances in diabetes care have facilitated the achievement and maintenance of excellent metabolic control. New insulin pumps and continuous glucose monitoring systems provide cause for optimism that an artificial pancreas may soon be developed. In addition, transplantation biology has advanced to the point where pancreas and islet transplants are being performed with increasing frequency. Recent reports suggest that improved techniques for isolation of islets and immune suppression may allow these procedures to become more commonplace. However, serious questions regarding long-term safety and efficacy need to be answered in older individuals before consideration is given to their more routine use in children and adolescents with diabetes.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.119
GPT teacher head0.375
Teacher spread0.255 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations11
Published2002
Admission routes1
Has abstractyes

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