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Record W1988670871 · doi:10.1097/tp.0b013e31820437f3

Reduced Progression of Diabetic Microvascular Complications With Islet Cell Transplantation Compared With Intensive Medical Therapy

2011· article· en· W1988670871 on OpenAlexaff
David Thompson, Mark Meloche, Ziliang Ao, Breay W. Paty, Paul Keown, R. Jean Shapiro, Stephen Ho, Dan Worsley, Michelle Fung, Graydon S. Meneilly, I S Begg, Mohammed Al Mehthel, J. Kondi, Claire Harris, Blake Fensom, Sharon Kozak, Suet On Tong, Mary Trinh, Garth L. Warnock

Bibliographic record

VenueTransplantation · 2011
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsThompson Rivers UniversityUniversity of British Columbia
Fundersnot available
KeywordsMedicineTransplantationDiabetic retinopathyRetinopathyDiabetes mellitusNephropathyInternal medicineDiabetic nephropathyProspective cohort studySurgeryEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: The effect of islet cell transplantation (ICT) on the progression of diabetic microvascular complications is not well understood. METHODS: We have conducted a prospective, crossover, cohort study comparing ICT with intensive medical therapy on the progression of diabetic nephropathy, retinopathy, and neuropathy. RESULTS: The rate of decline in glomerular filtration rate is slower after ICT than on medical therapy. There was significantly more progression of retinopathy in medically treated patients than post-ICT. There was a nonsignificant trend for improved nerve conduction velocity post-ICT. CONCLUSIONS: ICT is associated with less progression of microvascular complications than intensive medical therapy. Multicenter, randomized trials are needed to further study the role of ICT in slowing the progression of diabetic complications.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.261
Teacher spread0.234 · 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 designNon-randomized trial
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

Citations214
Published2011
Admission routes1
Has abstractyes

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