Clinical relevance of human leukocyte antigen antibodies in liver, heart, lung and intestine transplantation
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: Solid phase assays identify human leukocyte antigen (HLA) antibodies with a great sensitivity. Whether to accept or decline an organ if the virtual crossmatch is positive, when to monitor and whether to treat de-novo donor-specific antibody (DSA) posttransplant remain challenging issues for the transplant clinician. RECENT FINDINGS: Technologies that can differentiate which antibodies pose the greatest risk for antibody-mediated rejection (AMR) are evolving. Complement fixing luminex assays have been used to predict high-risk antibodies, but using these assays alone will miss some preformed antibodies. How these technologies fit into the laboratory's testing algorithm will likely need to be individualized. Posttransplant de-novo DSAs are associated with inferior outcomes. In hearts, similar to renal transplantation, acute rejection is a risk factor for developing de-novo DSA. Further data are needed to determine whether other risk factors are similar to those reported for renal transplants. Antibodies to self-antigens are increasingly recognized posttransplant and how the alloimmune response contributes to altered autoregulation is a current research focus. SUMMARY: Identification of DSA enables the clinician to make informed decisions regarding whether or not to accept an organ and if augmented immunosuppression is indicated. Monitoring for DSA posttransplant identifies recipients at a greater risk for AMR and can guide management. However, the best approach to dealing with de-novo DSA remains unclear.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| 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 | 0.000 |
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 it