Hypothermic machine perfusion improves <scp>D</scp> oppler ultrasonography resistive indices and long‐term allograft function after renal transplantation: a single‐centre analysis
Bibliographic record
Abstract
OBJECTIVES: To evaluate whether hypothermic machine perfusion (HMP) of transplanted kidneys can improve long-term renal allograft function compared with static cold storage (CS). METHODS: We evaluated whether graft Doppler ultrasonography resistive indices improved with the use of HMP compared with CS preservation, and examined whether these improvements were predictive of long-term graft function. A total of 30 kidney transplants (15 pairs) were examined. One of the kidney pairs was placed on CS and transplanted first (CS group, n = 15). The other kidney of each pair was placed on HMP and transplanted after the CS group (HMP group, n = 15). Doppler ultrasonography was performed on days 1 and 7 after transplantation and resistive indices were evaluated. The estimated glomerular filtration rate (eGFR) was monitored for 24 months after transplantation. RESULTS: Despite longer cold ischaemia times, kidneys maintained with HMP had lower resistive indices (P = 0.005) with correspondingly higher eGFR throughout the follow-up. Subgroup analysis showed that the HMP-induced improvement in postoperative eGFR was greatest in kidneys obtained from donation after cardiac death (DCD), even at 2 years after transplantation (P = 0.008). CONCLUSIONS: HMP of transplant kidneys appears to improve vascular resistance after transplantation and has a positive impact on long-term allograft function compared with CS in the population of recipients of DCD kidneys.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".