Human leukocyte antigen molecular mismatch to risk stratify kidney transplant recipients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE OF REVIEW: Stalled drug development and the lack of improvement in long-term graft survival reflect the unmet need for prognostic and predictive biomarkers in transplantation. Although conventional human leukocyte antigen (HLA) mismatch is too imprecise to fulfill this need, HLA molecular mismatch increases the precision in alloimmune risk assessment by quantifying the difference between donors and recipients at the molecular level. RECENT FINDINGS: Within each conventional HLA mismatch, recipients exhibit a wide range of HLA molecular mismatches with their donors. Quantifying HLA molecular mismatch improves the precision of alloimmune risk assessment for de novo donor-specific antibody development (dnDSA). Alloimmune risk categories developed analyzing dnDSA development were also found to correlate with T-cell-mediated rejection, antibody-mediated rejection, and all cause graft loss in adjusted and unadjusted models. SUMMARY: All alloimmunity is driven by differences between donors and recipients at the molecular level. HLA molecular mismatch may represent a fast, reproducible, cost-effective, way to improve alloimmune risk assessment at the time of transplantation to move the field towards precision medicine.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it