MétaCan
Menu
Back to cohort
Record W2899549084 · doi:10.3390/jcm7110425

Systematic Review and Meta-Analysis on the Impact of Thrombolytic Therapy in Liver Transplantation Following Donation after Circulatory Death

2018· review· en· W2899549084 on OpenAlexaff
Kumar Jayant, Isabella Reccia, Francesco Virdis, Anna Shapiro

Bibliographic record

VenueJournal of Clinical Medicine · 2018
Typereview
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineLiver transplantationTransplantationDonationIntensive care medicineSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Aim: The livers from DCD (donation after cardiac death) donations are often envisaged as a possible option to bridge the gap between the availability and increasing demand of organs for liver transplantation. However, DCD livers possess a heightened risk for complications and represent a formidable management challenge. The aim of this study was to evaluate the effects of thrombolytic flush in DCD liver transplantation. Methods: An extensive search of the literature database was made on MEDLINE, EMBASE, Cochrane, Crossref, Scopus databases, and clinical trial registry on 20 September 2018 to assess the role of thrombolytic tissue plasminogen activator (tPA) flush in DCD liver transplantation. Results: A total of four studies with 249 patients in the tPA group and 178 patients in the non-tPA group were included. The pooled data revealed a significant decrease in ischemic-type biliary lesions (ITBLs) (P = 0.04), re-transplantation rate (P = 0.0001), and no increased requirement of blood transfusion (P = 0.16) with a better one year graft survival (P = 0.02). Conclusions: To recapitulate, tPA in DCD liver transplantation decreased the incidence of ITBLs, re-transplantation and markedly improved 1-year graft survival, without any increased risk for blood transfusion, hence it has potential to expand the boundaries of DCD liver 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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.655
Threshold uncertainty score0.707

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0100.005
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.214
GPT teacher head0.499
Teacher spread0.285 · 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 designMeta-analysis
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

Citations30
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical MedicineSame topicOrgan Transplantation Techniques and OutcomesFrench-language works237,207