Microarray Diagnosis of Antibody-Mediated Rejection in Kidney Transplant Biopsies: An International Prospective Study (INTERCOM)
Bibliographic record
Abstract
In a reference set of 403 kidney transplant biopsies, we recently developed a microarray-based test that diagnoses antibody-mediated rejection (ABMR) by assigning an ABMR score. To validate the ABMR score and assess its potential impact on practice, we performed the present prospective INTERCOM study (clinicaltrials.gov NCT01299168) in 300 new biopsies (264 patients) from six centers: Baltimore, Barcelona, Edmonton, Hannover, Manchester and Minneapolis. We assigned ABMR scores using the classifier created in the reference set and compared it to conventional assessment as documented in the pathology reports. INTERCOM documented uncertainty in conventional assessment: In 41% of biopsies where ABMR features were noted, the recorded diagnoses did not mention ABMR. The ABMR score correlated with ABMR histologic lesions and donor-specific antibodies, but not with T cell-mediated rejection lesions. The agreement between ABMR scores and conventional assessment was identical to that in the reference set (accuracy 85%). The ABMR score was more strongly associated with failure than conventional assessment, and when the ABMR score and conventional assessment disagreed, only the ABMR score was associated with early progression to failure. INTERCOM confirms the need to reduce uncertainty in the diagnosis of ABMR, and demonstrates the potential of the ABMR score to impact practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".