Les temps forts de la transplantation rénale en 2023
Bibliographic record
Abstract
In 2023, significant advances were made in various areas of kidney transplantation. Firstly, the use of a balanced crystalloid solution in the recipient appears to prevent the delay in graft function, unlike hypothermia in the donor and normothermic pulsatile perfusion. Understanding the pathophysiology of humoral rejection has progressed, highlighting the major role of HLA class II molecules and innate immune cells (NK and monocytes expressing FCGR3A). An automatic Banff classification algorithm has been developed to better categorize biopsies in currently known diagnoses. CXCL10, combined with other variables, seems effective in ruling out rejection, but its role in routine care is yet to be defined. Regarding cytomegalovirus (CMV), letermovir has been proven effective in preventing CMV disease in D+R- patients, with fewer hematological side effects. For R+ patients, monitoring CMV-specific T-cell immunity is suggested to reduce the duration of antiviral prophylaxis. The only innovation in immunosuppression is imlifidase for highly sensitized patients, guided by French recommendations. A new equation for glomerular filtration rate measurement has been developed for kidney transplant recipients, performing well across various analyzed stratifications. Finally, xenotransplantation is making a comeback this year, generating hope. However, the description of early humoral rejections involving innate immune cells indicates that adjustments are still needed before considering its widespread deployment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".