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: To summarize the evidence concerning human leukocyte antigen (HLA) epitope mismatch analysis as a means to predict donor-specific antibody (DSA) development and allograft survival. RECENT FINDINGS: HLA epitope mismatch analysis outperforms traditional whole molecule antigen mismatch for predicting the risk of de-novo DSA development. By analyzing the number of epitope mismatches for a given donor-recipient pair, thresholds have been identified to stratify patients into those at high or low risk of de-novo DSA development. Epitope specificity assignment in patients who develop de-novo DSA compared with controls who do not provides an opportunity to study the relative immunogenicity of mismatched HLA epitopes. SUMMARY: Recognizing that de-novo DSA is a major cause of graft loss, HLA epitope mismatch analysis is a strategy to minimize de-novo DSA development and improve long-term graft survival.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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