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Record W2956018411 · doi:10.1097/txd.0000000000000915

Complement Markers in Blood and Urine: No Diagnostic Value in Late Silent Antibody-Mediated Rejection

2019· article· en· W2956018411 on OpenAlexaff
Blanka Mező, Andreas Heilos, Georg A. Böhmig, Farsad Eskandary, Markus Wahrmann, Gregor Bond, Nicolas Kozakowski, Philip F. Halloran, Krisztina Rusai, Zoltán Prohászka

Bibliographic record

VenueTransplantation Direct · 2019
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsThe Metabolomics Innovation CentreUniversity of Alberta
FundersNemzeti Kutatási Fejlesztési és Innovációs HivatalAustrian Science FundSemmelweis EgyetemEmberi Eroforrások MinisztériumaFonds National de la Recherche Luxembourg
KeywordsMedicineReceiver operating characteristicComplement systemContext (archaeology)AntibodyArea under the curveAlternative complement pathwayDonor specific antibodiesHuman leukocyte antigenUrineRenal functionCreatinineUrinary systemInternal medicineImmunologyGastroenterologyAntigenBiology

Abstract

Background. Antibody-mediated rejection (AMR) is a major cause of kidney allograft failure. Its molecular mechanisms are multifaceted and may include a role of complement activation via the classical pathway. Here, we investigated whether noninvasive complement monitoring adds predictive power to the diagnosis of AMR in the setting of donor-specific antibody (DSA) positivity. Methods. In this cross-sectional study, 741 kidney transplant recipients with stable graft function ≥180 days posttransplantation were screened for the presence of human leukocyte antigen (HLA) alloantibodies. Eighty-three of 111 DSA-positive recipients underwent protocol biopsies and were tested for blood and urinary levels of complement proteins (C1q, C4, C3) and activation products (C4d, C3a, C5a, C5b-9). Results. Forty-seven recipients were diagnosed with AMR, and 21 were C4d-positive. While biopsy-confirmed AMR (and C4d) associated with DSA-binding strength (IgG mean fluorescence intensity of the immunodominant DSA versus AMR; area under the receiver operating characteristic curve: 0.76), tested complement markers did not have any predictive value for rejection (area under the receiver operating characteristic curve: 0.49–0.56). There were, however, tight correlations between complement activation products in urine and protein/creatinine ratio ( ρ = 0.44–0.64; P < 0.001). Analysis of death-censored graft survival over a median of 60 months revealed no independent associations with levels of complement markers in blood or urine. Conclusions. Complement patterns in blood and urine failed to identify AMR in late biopsies and may have no relevant diagnostic value in this particular context.

Stored with the screening record, where it is evidence for the labels above.

How this classification was reachedexpand

The three-model screen

all 5,600 screened works →

All three models called this out of scope.

stratum: aff_core · design weight: 5595.24 (the sample is stratified; any rate computed without the weight is wrong)
Claude Opus 4.8OUT
genre: empirical
about Canada: no
confidence: high

Cross-sectional study of complement markers for diagnosing antibody-mediated kidney rejection; a clinical diagnostic question.

GPT-5.6 (high)OUT
genre: empirical
about Canada: no
confidence: high

It evaluates diagnostic markers for kidney-transplant rejection, not research itself.

Grok 4.5OUT
genre: empirical
about Canada: no
confidence: high

Diagnostic value of complement markers in kidney transplant rejection; clinical nephrology.

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)0.003
metaresearch head score (Gemma)0.009
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.276
Teacher spread0.265 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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