How to Embark Upon Leadership in Transplantation and Potential Pathways to Consider
Bibliographic record
Abstract
Physician engagement in leadership leads to better delivery of care to patients and is crucial for the advancement of knowledge, understanding, and wisdom in transplantation. Despite this, many physicians do not think of themselves as leaders and not much is offered in the form of training and education in leadership. Those who want to embark on a path to leadership sometimes do not know how to engage and where to start. This paper proposes 6 potential pathways to consider while embarking on leadership in transplantation. These are clinical innovation, research, education, administration, advocacy, and ethics. The profiles of some of the major leaders in the field of transplantation are highlighted to exemplify them. In addition, some other emerging pathways are presented. These proposed pathways are meant to serve as a guide on where to start but are interdependent. Last, how to choose from these options is described using techniques such as self-reflection, mentorship, peer engagement, and participation in leadership programs.
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 | 0.030 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".