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Record W2471546237 · doi:10.1097/mnh.0000000000000170

The perils of immunosuppression minimization

2015· review· en· W2471546237 on OpenAlexaff
David N. Rush, Ian W. Gibson

Bibliographic record

VenueCurrent Opinion in Nephrology & Hypertension · 2015
Typereview
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsUniversity of Manitoba
FundersWellcome TrustMayo Clinic
KeywordsImmunosuppressionMedicineSubclinical infectionImmunologyHuman leukocyte antigenInflammationChemokineTacrolimusAntigenTransplantationPathologyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: To emphasize the pathogenicity of subclinical cellular inflammation in renal transplant recipients, and its relation to poor graft outcomes and the development of de-novo donor-specific antibody (DSA). RECENT FINDINGS: Protocol biopsies have identified the gene signatures of innate and adaptive immunity in patients with minimal inflammation that correlate with the subsequent development of graft interstitial fibrosis, transplant glomerulopathy and antibody-mediated rejection. The risks of immunosuppression minimization, especially in HLA mismatched donor-recipient pairs, are highlighted. SUMMARY: The major cause of renal allograft loss is immunological and a contributor to this is the minimization of immunosuppression. The prevention of premature graft loss requires better matching of class II HLA antigens, the targets of de-novo DSA, and monitoring for subclinical inflammation rejection with protocol biopsies or urine chemokines.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.151
GPT teacher head0.414
Teacher spread0.263 · 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 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

Citations4
Published2015
Admission routes1
Has abstractyes

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