MétaCan
Menu
Back to cohort
Record W4388221078 · doi:10.1097/mou.0000000000001139

Organ storage in renal transplantation

2023· review· en· W4388221078 on OpenAlexaff
Yanbo Guo, Patrick Luke, Alp Şener

Bibliographic record

VenueCurrent Opinion in Urology · 2023
Typereview
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsWestern UniversityMcMaster University
Fundersnot available
KeywordsMedicineMachine perfusionIntensive care medicineTransplantationExcellenceKidney transplantationKidneyPerfusionLiver transplantationSurgeryInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Kidney transplantation is vital for those with end-stage renal disease, enhancing quality of life and longevity. It is the preferred treatment but is hindered by a global disparity between donor kidney availability and demand. Therefore, optimizing organ storage techniques is crucial to mitigate the effects of ischemia reperfusion injury in available organs. Recent interest has centered on innovative methods like oxygenated normothermic perfusion and abdominal regional perfusion. RECENT FINDINGS: Multiple recent metanalyses, including a Cochrane review, confirm the benefits of hypothermic machine perfusion (HMP) for deceased donor kidneys, demonstrating its utility and cost effectiveness. The benefits of oxygenated normothermic perfusion have been seen in retrospective data sets but not in prospective trials. Abdominal regional perfusion (aNRP) is gaining interest, especially for liver transplantation, but kidney specific data are scant. SUMMARY: High-quality evidence backs the use of HMP for deceased donor kidneys. Despite interest in other techniques, clinical evidence for their benefits in kidney transplantation is lacking. The gap between innovation and verified success emphasizes the need for continued research and collaboration between medical professionals, researchers, and ethical committees. This review aims to further illuminate the complexities and advancements in the field, bridging the knowledge gap and aiding in the continual pursuit of excellence in 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.138
GPT teacher head0.439
Teacher spread0.301 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCurrent Opinion in UrologySame topicOrgan Transplantation Techniques and OutcomesFrench-language works237,207