Use of Personalized Medicine in the Selection of Patients for Renal Transplantation: Views of Quebec Transplant Physicians and Referring Nephrologists
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it