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Record W2438056874 · doi:10.1111/ctr.12780

Heparin infusion in simultaneous pancreas and kidney transplantation reduces graft thrombosis and improves graft survival

2016· article· en· W2438056874 on OpenAlexaff
Ghaleb Aboalsamh, Patrick Anderson, Amira Al‐Abbassi, Vivian C. McAlister, Patrick Luke, Alp Şener

Bibliographic record

VenueClinical Transplantation · 2016
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineThrombosisSurgeryHeparinPancreas transplantationComplicationTransplantationRetrospective cohort studyAnesthesiaKidney transplantation

Abstract

fetched live from OpenAlex

INTRODUCTION: Thrombosis of the pancreas after transplantation is the most common cause of relaparotomy and resultant graft loss. There is currently no standard protocol consistently proven to prevent thrombosis following transplantation. Our objective was to determine whether our protocol of post-operative low-dose intravenous (IV) heparin infusion would prevent graft thrombosis without additional complications in our patients. METHODS: A total of 66 simultaneous pancreas kidney (SPK) transplants were performed at our institution from 2004 to 2014. Patients were divided into 2 retrospective cohort groups. Group 1 patients received only acetylsalicylic acid (ASA) 81 mg/d started on post-operative day 1. Group 2 patients received IV heparin infusion beginning in the recovery room at a rate of 500 IU/h for the first 24 hours, reduced by 100 IU/h every day to stop on day 5, and then received ASA 81 mg/d afterward. Outcome and complication rates were compared between the two groups for 5 years post-transplant. RESULTS: We observed a significant reduction in graft thrombosis and graft loss with (0/29) patients in the heparin group vs (7/33) 25.7% from the non-heparin (P<.01) with no differences in complication rates. CONCLUSIONS: We present a heparin infusion protocol which may help prevent graft thrombosis and graft loss in SPK transplantation.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.076
Threshold uncertainty score0.743

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.049
GPT teacher head0.366
Teacher spread0.317 · 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 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

Citations27
Published2016
Admission routes1
Has abstractyes

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