The use of personalized medicine for patient selection for renal transplantation: Physicians' views on the clinical and ethical implications
Bibliographic record
Abstract
BACKGROUND: The overwhelming scarcity of organs within renal transplantation forces researchers and transplantation teams to seek new ways to increase efficacy. One of the possibilities is the use of personalized medicine, an approach based on quantifiable and scientific factors that determine the global immunological risk of rejection for each patient. Although this approach can improve the efficacy of transplantations, it also poses a number of ethical questions. METHODS: The qualitative research involved 22 semi-structured interviews with nephrologists involved in renal transplantation, with the goal of determining the professionals' views about calculating the global immunological risk and the attendant ethical issues. RESULTS: The results demonstrate a general acceptance of this approach amongst the participants in the study. Knowledge of each patient's immunological risk could improve treatment and the post-graft follow-up. On the other hand, the possibility that patients might be excluded from transplantation poses a significant ethical issue. This approach is not seen as something entirely new, given the fact that medicine is increasingly scientific and evidence-based. Although renal transplantation incorporates scientific data, these physicians believe that there should always be a place for clinical judgment and the physician-patient relationship. CONCLUSIONS: The participants see the benefits of including the calculation of the global immunological risk within transplantation. Such data, being more precise and rigorous, could be of help in their clinical work. However, in spite of the use of such scientific data, a place must be retained for the clinical judgment that allows a physician to make decisions based on medical data, professional expertise and knowledge of the patient. To act in the best interests of the patient is key to whether the calculation of the global immunological risk is employed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.130 | 0.165 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.035 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 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".