Using individual <scp>DSA</scp> titers to assess for accommodation after late humoral rejection
Bibliographic record
Abstract
Management of late humoral rejection remains challenging, and DSA may persist. A case report illustrates how individual DSA titers using solid-phase-based assays may help to assess for accommodation. A male cystinosis patient received a cadaveric renal transplant at the age of 12 yr with a daclizumab, tacrolimus, MMF, and steroids-based immunosuppression. After three acute rejection episodes over the first eight months, interstitial fibrosis/tubular atrophy (IF/TA) was diagnosed on biopsy, while the immunosuppression was left unchanged with a high target exposure for both tacrolimus and MPA. One yr later, AMR type III (C4d and DSA positive) was treated with daily plasmapheresis, IVIG 100 mg/kg and pulse steroids 5 mg/kg. DSA (DR 53, DQ4, and DQ 2) were not responding until the plasma volume was increased to 2.5 plasma volumes. A second rise of creatinine confirmed worse humoral rejection; daily plasma exchange was resumed, and two doses of rituximab (375 mg/m(2)) were given. Subsequently, all DSA dropped, but only DR53 DSA remained unchanged, whereas the DQ antibodies rebounded to very strong titers. With a follow-up of over 120 days after recovery of the CD19 count, off all additional treatment and on identical immunosuppression with tacrolimus and MMF and prednisone, the patient's creatinine remained stable between 45 and 50 um while DQ DSA remain strong to very strong. We conclude that the patient is in a state of accommodation. DSA titers should be monitored when managing late humoral rejection.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".