Evaluation of the Iranian panel reactive antibody calculator and potential usefulness: A retrospective study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: There are several cPRA websites based on large enough samples in Eurotransplant, the United Network for Organ Sharing (UNOS), and the Canadian Transplant Registry (CTR). On the other hand, those calculators can differ based on the ethnicity to which they are applied. We developed the Iranian PRA calculator and compared it with UNOS and CTR calculators. METHODS: The allele and haplotype frequencies of the Iranian donor pool were estimated using the HLA typing of 523 deceased Iranian kidney donors. The Organ Procurement and Transplantation Network formula was used to generate cPRA (cPRA frequency). We also used a computer script to compare the undesirable antigens of patients with the human leukocyte antigen (HLA) phenotype of donors (cPRA filtering). A total of 100 anti-HLA antibody profiles were determined in 100 sensitized individuals on the waiting list, and cPRA was estimated using various PRA calculators. RESULTS: Variable allelic frequencies were obtained from population heterogeneity in each calculator's donor panel. However, no significant changes in cPRA were identified between the Iranian calculator, UNOS, and the Canadian online calculators. Lin's concordance correlation coefficient of .98 showed that cPRA (freq) and cPRA (filter) values had almost perfect agreement. INTERPRETATION AND CONCLUSION: The cPRA values from the Iranian calculator are comparable to those from UNOS and CTR calculators. The donor filtering method was more useful because of factors like cost and flexibility. It also makes it easier to update cPRA on a regular basis.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".