MétaCan
Menu
Back to cohort
Record W88336499

Living-donor kidney transplantation at Mayo Clinic--Rochester.

2002· article· en· W88336499 on OpenAlexaff
Mark D. Stegall, Timothy S. Larson, Mikel Prieto, James M. Gloor, Stephen C. Textor, Scott L. Nyberg, Sylvester Sterioff, Michael B. Ishitani, Matthew D. Griffin, Thomas R. Schwab, Sandra Talor, Fernando G. Cosio, Yogish C. Kudva, Deborah Dicke-Henslin, Michelle Kreps, Lynette Fix, Cherise Bauer, Mary E. Murphy, Kay Kosberg, Deborah Tarara, Jorge A. Velosa

Bibliographic record

VenuePubMed · 2002
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsInstitute of Nutrition, Metabolism and Diabetes
Fundersnot available
KeywordsMedicineImmunosuppressionTransplantationChronic allograft nephropathyKidney transplantationSubclinical infectionCalcineurinKidneyNephrectomyUrologySurgeryInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

With the established benefits of living-donor kidney transplantation, our primary emphasis at Mayo Clinic, Rochester has been to develop protocols that allow living donation to occur even in the presence of relatively unusual or generally contraindicated situations. This approach has significantly increased the number of patients receiving kidney transplants in the past few years. Our protocols for extended criteria donors and recipients along with the exclusive use of laparoscopic donor nephrectomy have been major contributors to the increase in volume. ABO-incompatible and positive-crossmatch living-donor kidney transplant protocols also have increased the availability of transplants for our patients. Protocol biopsies have aided in the diagnosis of subclinical rejection, polyoma virus and chronic allograft nephropathy. Innovative immunosuppressive protocols such as calcineurin inhibitor-free immunosuppression have decreased rejection and improved both short and long-term renal allograft survival.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.056
Threshold uncertainty score0.189

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0560.018

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 head0.037
GPT teacher head0.240
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2002
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicOrgan Donation and TransplantationFrench-language works237,207