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Protocol Biopsies in Renal Transplantation

2012· article· en· W2136890491 on OpenAlexaff
David N. Rush

Bibliographic record

VenueBENTHAM SCIENCE PUBLISHERS eBooks · 2012
Typearticle
Languageen
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsUniversity of ManitobaHealth Sciences Centre
Fundersnot available
KeywordsMedicineSubclinical infectionTransplantationBiopsyImmunosuppressionKidney transplantationRenal biopsyPathologyChronic allograft nephropathyInternal medicine

Abstract

fetched live from OpenAlex

Protocol biopsies in renal transplantation are those that are procured at predetermined times post renal transplantation, regardless of renal function. These biopsies have been useful to study the natural history of the transplanted kidney as they have detected unexpected - i.e. "subclinical" pathology. The most significant subclinical pathologies that have been detected with protocol biopsies have been acute lesions, such as cellular and antibody mediated rejection, and chronic lesions, such as interstitial fibrosis and tubular atrophy, and transplant glomerulopathy. The potential benefit of early recognition of the above lesions is that their early treatment may result in improved long-term outcomes. Conversely, the identification of normal histology on a protocol biopsy, may inform us about the safety of reduction in overall immunosuppression. Our centre, as well as others, is attempting to develop non-invasive methods of immune monitoring of renal transplant patients. However, we believe that until such methods have been developed and validated, the protocol biopsy will remain an indispensable tool for the complete care of renal transplant patients.

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)0.010
metaresearch head score (Gemma)0.014
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: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.026
GPT teacher head0.314
Teacher spread0.289 · 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

Citations11
Published2012
Admission routes1
Has abstractyes

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