MétaCan
Menu
← Back to cohort
Record W2104431799 · doi:10.1210/jc.2014-4165

Percutaneous Autologous Pancreatic Islet Cell Transplantation for Traumatic Pancreatic Injury

2015· article· en· W2104431799 on OpenAlexaff
Avnesh S. Thakor, Bippan S. Sangha, Stephen Ho, Garth L. Warnock, Mark Meloche, David Liu

Bibliographic record

VenueThe Journal of Clinical Endocrinology & Metabolism · 2015
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineIsletTransplantationPancreatic ductPancreatectomyIslet cell transplantationDiabetes mellitusPercutaneousSurgeryPancreasInternal medicinePancreatitisGastroenterologyEndocrinology

Abstract

fetched live from OpenAlex

CONTEXT: Traumatic pancreatic injury with pancreatic duct disruption is surgically managed with at least a partial pancreatectomy, often leading to poor blood glucose control and the subsequent development of diabetes mellitus. Autologous β-islet cell transplantation may therefore help to preserve pancreatic endocrine function. CASE DESCRIPTION: We describe 3 patients with pancreatic duct disruption from traumatic pancreatic injury who were treated with a partial pancreatectomy followed by autologous β-islet cell transplantation via a percutaneous transhepatic approach. Immediately after trauma, 2 of the 3 patients had difficulty with glucose control that resolved after autologous β-islet cell transplantation. At follow-up, all patients remained normoglycemic. CONCLUSION: In patients requiring partial pancreatectomy after pancreatic trauma, percutaneous transhepatic autologous β-islet cell transplantation should be considered to minimize the risk of development of 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 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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.068
GPT teacher head0.367
Teacher spread0.300 · 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 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

Citations13
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Clinical Endocrinology & Metabolism→Same topicPancreatic function and diabetes→French-language works237,207→