Evaluation of C1q Status and Titer of De Novo Donor-Specific Antibodies as Predictors of Allograft Survival
Bibliographic record
Abstract
De novo donor-specific antibodies (dnDSAs) that develop after renal transplantation are independent predictors of allograft loss. However, it is unknown if dnDSA C1q status or titer at the time of first detection can independently predict allograft loss. In a consecutive cohort of 508 renal transplant recipients, 70 developed dnDSAs. Histologic and clinical outcomes were correlated with the C1q assay or dnDSA titer. C1q positivity correlated with dnDSA titer (p < 0.01) and mean fluorescence intensity (p < 0.01) and was more common in class II versus class I dnDSAs (p < 0.01). C1q status correlated with tubulitis (p = 0.02) and C4d status (p = 0.03) in biopsies at the time of dnDSA development, but not T cell-mediated rejection (TCMR) or antibody-mediated rejection (ABMR). De novo DSA titer correlated with Banff g, i, t, ptc, C4d scores, TCMR (p < 0.01) and ABMR (p < 0.01). Post-dnDSA graft loss was observed more frequently in recipients with C1q-positve dnDSA (p < 0.01) or dnDSA titer ≥ 1:1024 (p ≤ 0.01). However, after adjustment for clinical phenotype and nonadherence in multivariate models, neither C1q status nor dnDSA titer were independently associated with allograft loss, questioning the utility of these assays at the time of dnDSA development.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".