Use of Personalized Medicine in the Selection of Patients for Renal Transplantation: Views of Quebec Transplant Physicians and Referring Nephrologists
Bibliographic record
Abstract
AIM: To explore the views of physicians on the use of personalized medicine tools to develop a new method for selecting potential recipients of a renal allograft. METHODS: A total of 22 semidirected interviews, using clinical case studies. RESULTS: According to the participants, this method has several possible applications within renal transplantation (individualizing immunosuppressive therapy, help with decision making, and possibly with the selection of patients). It could be more effective than the method presently used. The method must be validated scientifically, and must also involve clinical judgment. CONCLUSION: The use of personalized medicine within transplantation must be in the best interests of the patient. An ethical reflection is necessary in order to focus on the possibility of patients being excluded, as well as on the resolution of the equity/efficacy dilemma. Empirical research has shown itself to be essential for ascertaining the views of the clinicians who will be working with the tools provided by personalized medicine.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".