Clinical and molecular spectrum of v-lesion
Bibliographic record
Abstract
Isolated v-lesion presents diagnostic stratification and clinical challenges. We characterized allograft outcomes for this entity based on posttransplant time (early: ≤1 month vs late: >1 month) and compared its molecular phenotype with other v+ rejection forms. Using the NanoString B-HOT panel, we analyzed 92 archival formalin-fixed paraffin-embedded tissue kidney biopsies from 3 centers: isolated v-lesion (n = 23), antibody-mediated rejection (ABMR) v+ (n = 26), T cell-mediated rejection (TCMR) v+ (n = 10), mixed rejection v+ (n = 23), and normal tissue (n = 10). Six gene sets (ABMR, DSAST, ENDAT, TCMR, early/acute injury, late injury) were assessed. Early isolated v-lesions had the poorest 1-year death-censored graft survival compared with late isolated v-lesions or other rejections (P = .034). Gene set analysis showed lower TCMR-related gene expression in isolated v+ groups than TCMR and mixed rejection (P < .001). Both early- and late isolated v-lesions had lower ABMR-related gene expression than ABMR, mixed rejection, and TCMR (P ≤ .022). Late isolated v-lesions showed reduced DSAST and ENDAT gene expression versus ABMR (P ≤ .046) and decreased early/acute injury gene expression than early isolated v+, ABMR, TCMR, and mixed rejection (P ≤ .026). In conclusion, isolated v-lesions exhibit distinct gene expression patterns versus other rejection v+ forms. Early isolated v+ is associated with poorer prognosis and increased early/acute injury gene expression than late isolated v+, suggesting distinct etiologies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".