Calculated Panel Reactive Antibody for an Organ-Sharing Zone: Concept and Execution Based on Data from North Kerala
Bibliographic record
Abstract
Background Calculated-PRA (CPRA) is derived by virtually matching a recipient’s antibody profile against HLA antigens of a representative donor pool of a geographical region. Materials and Methods An android application–based CPRA calculator was created from HLA typing data (A/B/DR loci) of 712 consecutive living donors spanning over last 10 years from an organ-sharing region in Kerala. Only an open-source software was used. Results Our HLA data, compared with the National Marrow Donor Program (NMDP) 2011 database, show that the most common haplotype frequencies show comparable positions in order of prevalence. Available online PRA calculators like OPTN and Canadian CPRA show significant differences in PRA estimation when used in our population. This calculator provides a more accurate and realistic estimate of sensitization against the representative donor pool. Conclusion This CPRA tool can be customized for any allocation region using portable open-source software. The donor pool can be updated continually by populating data from multiple regional centers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".