221.6: Development of a calculated panel reactive antibody calculator for the United Arab Emirates.
Bibliographic record
Abstract
Calculated panel reactive antibody (CPRA) is used to help increase sensitized patient’s access to transplantation. United Arab Emirates (UAE) has a diverse resident population hence we developed a UAE–CPRA calculator based on HLA antigen frequencies of the different ethnic groups that represent the UAE population. HLA antigen frequencies at serological split antigen level for HLA-A, -B, -C, -DRB1 and -DQB1 of 1002 healthy unrelated donors were performed. We subsequently compared the performance of the UAE CPRA calculator with the Organ Procurement and Transplantation Network (OPTN) and the Canadian CPRA calculators in 110 Kidney Transplant waitlist patients from January 2016 to December 2018. Lin’s concordance correlation coefficient showed a moderate agreement between the UAE and OPTN calculator (Rc = 0.949, 95% CI 0.929–0.963) and the UAE and Canadian calculators (Rc = 0.952, 95% CI 0.932–0.965). While there continued to be a moderate agreement (Rc = 0.937, UAE versus OPTN calculator) in the lower sensitized group, a poor agreement (Rc = 0.555, UAE versus OPTN calculator) was observed in the higher sensitized group. In this study, we provide a template for countries to develop their own population-specific CPRA calculator. Implementation of the CPRA algorithm based on HLA frequencies of the multi-ethnic UAE population will be more fitting to increase access to transplantation and improve transplant outcomes. Our study demonstrates that the CPRA calculators developed using the data from the western population had poor correlation in our higher sensitized patients disadvantaging them in potential organ allocations systems. We plan to further refine this calculator by using high resolution HLA typing to address the problem of a genetically diverse population.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".