MétaCan
Menu
Back to cohort
Record W2253194402 · doi:10.12688/f1000research.7343.1

Innovative Applications of Robotic Surgery: Renal Allograft and Autologous Transplantation

2016· preprint· en· W2253194402 on OpenAlexaff
Jason Lee, Michael Ordon

Bibliographic record

VenueF1000Research · 2016
Typepreprint
Languageen
FieldMedicine
TopicRenal and Vascular Pathologies
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineTransplantationRobotic surgerySurgeryReconstructive surgeryUrinary systemKidney transplantationDiseaseRenal functionEnd stage renal diseaseInternal medicineHemodialysis

Abstract

fetched live from OpenAlex

Robotic surgery has enabled surgeons to offer more patients a minimally invasive surgical option in the management of their complex diseases. While renal transplantation is associated with significant improvements in quantity and quality of life for most end-stage renal disease (ESRD) patients, it is also not devoid of its surgical risks and potential morbidities. Robotic-assisted kidney transplantation is a recently described, innovative application of the robotic surgery platform, and early experiences suggest that it is associated with comparable graft function and lower rates of complications. Urinary tract obstruction, though less common than ESRD, can be a serious threat to renal function. Severe ureteric stricture disease can represent a clinically complex problem requiring major reconstructive surgery. Completely intra-corporeal robotic renal auto-transplantation is another innovative application of the robotic surgery platform and represents a significant advancement in urologic surgery. Initial reports of this procedure demonstrate safety, feasibility, and excellent renal function outcomes.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.671
Threshold uncertainty score0.469

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.061
GPT teacher head0.358
Teacher spread0.297 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueF1000ResearchSame topicRenal and Vascular PathologiesFrench-language works237,207