Results of a survey of the use of perfusion parameters as a selection tool for pumped deceased donor kidneys in the Organ Procurement Organizations of North America
Bibliographic record
Abstract
Abstract Background Hypothermic machine perfusion (HMP) has become a standard method for preserving deceased donor kidneys, offering advantages over static cold storage. Perfusion parameters like renal vascular resistance (RR) have been explored as potential decision-making tools for kidney transplantability, but their clinical use remains unclear. Aim We aimed to investigate the use of perfusion parameters in decision-making regarding the acceptance of pumped deceased donor kidneys among Organ Procurement Organizations (OPOs) in the USA and Canada. Methods An anonymous, internet-based survey was sent to 69 OPOs in the USA and Canada, collecting data on the use of HMP, perfusion parameters, and thresholds for transplantability decisions. Descriptive statistics were used for analysis. Results Of the 67 OPOs contacted, 15 (22%) responded, with 13 complete responses (87%). All OPOs used HMP, with 93% perfusing both donation after brain death and circulatory death kidneys. While 97% of OPOs used perfusion parameters in decision-making, none relied solely on these parameters. Two OPOs (15%) did not use them at all, while six OPOs (46%) considered them with other data or on a case-by-case basis. Only one OPO (9%) reported using specific thresholds for perfusion parameters, applying flow ≥100 mL/min, resistance <0.3 mmHg/mL/min, and pressure between 15–35 mmHg. Conclusion HMP is widely used, but substantial variability exists in the use of perfusion parameters for transplant decisions. Most OPOs do not rely on these parameters alone and lack standardized thresholds, though specific thresholds are still used.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".