MétaCan
Menu
Back to cohort
Record W2031696333 · doi:10.1097/tp.0b013e31823e46ef

Further Evidence for Amyloid Deposition in Clinical Pancreatic Islet Grafts

2011· article· en· W2031696333 on OpenAlexaff
Gunilla T. Westermark, Alberto M. Davalli, Antonio Secchi, Franco Folli, Tatsuya Kin, Christian Toso, A. M. James Shapiro, Olle Korsgren, Gunnar Tufveson, Arne Andersson, Per Westermark

Bibliographic record

VenueTransplantation · 2011
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsUniversity of Alberta
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of Allergy and Infectious DiseasesSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsIsletAmyloid (mycology)MedicinePathologyDiabetes mellitusEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: The reasons for the long-term complete or partial loss of islet graft function are unknown, but there are obviously other reasons than just pure allogeneic graft rejection. Earlier studies have shown that deposition of islet amyloid polypeptide amyloid in transplanted islets may indicate a mechanism for loss of β cells. MATERIALS AND METHODS: Sections from liver material from four deceased islet-bearing recipients have been scrutinized for the presence of amyloid. Clinical data and certain aspects of the islet graft pathology of these patients have been published previously. RESULT: With this extended histological analysis, we demonstrate the occurrence of amyloid deposits in islets transplanted into the liver in three of four patients with type 1 diabetes. CONCLUSION: The finding adds evidence to the assumption that aggregation of islet amyloid polypeptide might be an important cause of progressing β-cell dysfunction in clinically transplanted islets.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.125
GPT teacher head0.358
Teacher spread0.233 · 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".

Quick stats

Citations47
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueTransplantationSame topicPancreatic function and diabetesFrench-language works237,207