MétaCan
Menu
Back to cohort
Record W2946481123 · doi:10.1155/2019/9617087

Pharmacological Protection of Kidney Grafts from Cold Perfusion-Induced Injury

2019· review· en· W2946481123 on OpenAlexaff
Anna Krzywonos‐Zawadzka, Aleksandra Franczak, Mike Moser, Agnieszka Olejnik, Grzegorz Sawicki, Iwona Bil‐Lula

Bibliographic record

VenueBioMed Research International · 2019
Typereview
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsUniversity of Saskatchewan
FundersNarodowe Centrum Nauki
KeywordsMedicineKidneyTransplantationDialysisAcute kidney injuryKidney transplantationEconomic shortageIntensive care medicineUrologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

One of the greatest challenges facing the field of organ transplantation is the shortage of donor organs for transplantation. Renal transplantation increases quality of life and survival of patients suffering from end-stage renal disease. Although kidney transplantation has evolved greatly over the past few decades, a not insignificant amount of injury occurs to the kidney during recovery, preservation, and implantation and leads to the loss of function and loss of years of dialysis-free living for many patients. The use of kidneys from expanded criteria donors (ECD) and donation after circulatory determination of death (DCDD) has been adopted partly in response to the shortage of donor kidneys; however these kidneys are even more susceptible to ischemic injury. It has been shown that matrix metalloproteinases (MMPs) and reactive oxygen species (ROS) are involved in mechanisms of injury to the transplant kidney. There is also some evidence that inhibition of MMP activity and/or ROS production can protect the kidney from injury. We review possible pharmacological strategies for protection of kidney graft from injury during recovery, preservation, and implantation.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.930
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0030.000

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 head0.299
GPT teacher head0.517
Teacher spread0.218 · 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.

Study designNot applicable
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

Citations8
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueBioMed Research InternationalSame topicOrgan Transplantation Techniques and OutcomesFrench-language works237,207