Portuguese calculated panel reactive antibodies online estimator
Bibliographic record
Abstract
Calculated panel reactive antibodies (CPRA) is a sensitization measure used to classify and prioritize transplant candidates in different kidney transplant allocation systems. CPRA is based on identification of HLA unacceptable on potential organ donors making a transplant candidate ineligible for transplantation. Here, we present a CPRA online estimator based on HLA allelic and haplotypic frequencies from Portuguese donors. We also compare the values we obtained from our CPRA estimator (Portuguese [PT]-CPRA) against CPRA values obtained from: Eurotransplant virtual PRA calculator (ET-CPRA); Canadian CPRA calculator (Canadian [CN]-CPRA) and Organ Procurement and Transplantation Network CPRA calculator (United States [US]-CPRA). When we analyzed correlations between CPRA values obtained from pairs of calculators, we observed that they are significantly and highly correlated. Bland-Altman plots for the comparison between PT-CPRA calculator against the other calculators, show higher differences between PT and CN than between PT and ET and PT and US. Also, the lowest value for Lin's concordance coefficient was obtained for the comparison between PT and CN calculators. CPRA values reliability depend on donors' pool from which it is calculated and it is crucial for classify correctly highly sensitized patients. A CPRA calculator must use donors' HLA frequencies similar from those who would be actual organ donors.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".