Minimum mycophenolic acid levels are associated with donor‐specific antibody formation
Bibliographic record
Abstract
Although de novo DSA are associated with inferior graft survival, there are no effective strategies to prevent their formation. Underexposure to MPA (prodrug: MMF) also contributes to rejection rates early after transplantation, but the effect of this phenomenon on the formation of DSA long-term post-transplantation is unknown. Data are expressed as mean (standard deviation). All available data from 32 renal transplant recipients (age at transplantation 7.5 [4.5] yr) on tacrolimus and MPA immunosuppression with an average follow-up of 9.4 (s.d. 4.6) yr were analyzed. DSA were measured using the Luminex assay (>500 MFI was considered DSA-positive). Tacrolimus and MPA levels were measured with the Abbot Tacro II and EMIT assay, respectively. Among 1964 MPA and 3462 tacrolimus trough levels, the average MPA trough level was 3.2 (1.5) mg/L and the average tacrolimus level was 6.7 (2.8) ng/mL. At last follow-up, only 5/32 patients had undetectable DSA, with 5/32 having no class I antibodies and 6/32 having no class II antibodies. DSA formation was associated with a lower minimum MPA trough level (0.27 [0.23] vs. 0.47 [0.18] mg) and cystatin C eGFR (48 [21] vs. 70 [23] mL/min/1.73 m(2)) for class I DSA formers. The average eGFR of patients without class I DSA was 70 (23) mL/min/1.73 m(2), whereas the average eGFR of patients with class I DSA was 48 (21) mL/min/1.73 m(2) (p = 0.0071). MPA trough levels <1.3 mg/L long-term post-transplantation are associated with the formation of DSA. The association between the formation of DSA and minimum MPA exposure may support a strategy for preventing the formation of DSA.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".