Bibliographic record
Abstract
PURPOSE OF REVIEW: Current immune risk criteria for selecting induction therapy lack precision. Here, we examined the relationship of human leukocyte antigen (HLA) and molecular matching with outcomes in patients treated with different induction regimens and immunosuppressive minimization protocols to inform their potential utility in guiding therapy. RECENT FINDINGS: Initial studies evaluating induction therapy suggest the role of HLA matching in immune risk-stratification. However, criteria based on antigen level matching and panel-reactive antibodies are imprecise and risk over-assigning patients to treatment with T-cell-depleting agents. Molecularly defined low-risk patients comprise 19-61% of study cohorts. Across heterogenous induction regimens and immunosuppressive minimization studies, these patients consistently demonstrated low immune event rates, providing the basis for prospective trials to test its utility in guiding the choice of induction regimens. SUMMARY: Granular assessment of immune compatibility using molecular mismatch methods coupled with rapid genotyping technologies may help improve the selection of immunosuppressive regimens but will require prospective confirmation.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".