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Addressing The Unmet Need For Precision Diagnosis: Potential Impact of Molecular Assessments On Conventional Biopsy Diagnostics in 41% of 700 Indication Biopsies.

2014· article· en· W2772497207 on OpenAlexaffabout
Philip F. Halloran, J. Reeve, Alexandre Loupy, Carmen Lefaucheur

Bibliographic record

VenueTransplantation · 2014
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsThe Metabolomics Innovation Centre
Fundersnot available
KeywordsMedicineMedical diagnosisBiopsyInternal medicineRadiology

Abstract

fetched live from OpenAlex

When transplants develop problems, treatment depends critically on accurate assessment of antibody-mediated and T cell-mediated rejection (ABMR, TCMR). But conventional assessments have problems, including the poor reproducibility of pathologist opinions e.g. 50% sensitivity for TCMR diagnoses in rejecting biopsies, and variability in DSA measurement. The addition of objective molecular measurements could add precision and guide therapy. We compared conventional and molecular assessments in 700 indication biopsies, prospectively collected 3 days to 31 years post transplant between 2004-13, including 291 from INTERCOM, 242 from Genome Canada, 81 from Hopital St Louis, Paris, and 86 new biopsies from INTERCOM centers. All were diagnosed conventionally and by the Molecular Microscope (MM) system using new Affymetrix U219 microarrays. The U219 classifiers were built using Genome Canada and INTERCOM samples with published methods. MM diagnoses strongly correlated with conventional diagnoses (table 1) but offered many new insights.Table: No Caption available.In 104 conventional ABMR, MM found 58 ABMR, 5 TCMR, 6 mixed, and 35 with no rejection (accuracy 82%). In 25 called TG but not ABMR, MM found 9 ABMR and 1 TCMR. In 77 conventional TCMR, MM found 37 TCMR, 8 mixed, 6 ABMR, and 9 with no rejection (accuracy 84%). In 32 with conventional mixed, MM found 24 (75%) were not mixed. MM assigned categories to 104 biopsies where conventional assessment was explicitly indeterminate, including 35 called suspicious for ABMR and all 79 Borderline. Moreover, in 20 with BK, MM found TCMR in 11. In 306 conventionally assessed as no rejection or BK, the MM found 26 ABMR, 12 TCMR, and 2 mixed. Thus in 285 (41%) of 700 biopsies, the addition of MM scores for ABMR and TCMR had potential to change management e.g. finding TCMR in BK cases, making diagnoses in “borderline” and “suspicious” cases, and suggesting that most “mixed” is not mixed. Thus molecular diagnosis, while not necessarily always correct, adds a new dimension to the understanding of troubled transplants in critical situations, addressing a major unmet need. DISCLOSURE:Halloran, P.: Other, Astellas, Lecturing, One Lambda, Lecturing, Novartis, Lecturing.

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)0.012
metaresearch head score (Gemma)0.026
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 score0.013
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.052
GPT teacher head0.390
Teacher spread0.338 · 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".

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Citations1
Published2014
Admission routes2
Has abstractyes

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