The Impact of Mentoring Our Future Leaders: 12 Years of the Astct Clinical Research Training Course
Bibliographic record
Abstract
The ASTCT Clinical Research Training Course (CRTC) is designed to build physician capacity and retain trainees/junior faculty in academic cell therapy careers. Participation is on a competitive basis. Applicants submit a clinical study proposal, career plan, CV and mentor letter of support. Each year 10-12 scholars join ∼10 senior faculty including statistician(s) for 5 days of lectures, small group work developing their protocols, career guidance and mentoring. We reviewed the course to evaluate whether it was meeting its mission. Scholars were invited to participate in an online survey and submit their cv. Scholars were asked to rate the impact of the course on their career. Data was extracted from CVs to measure academic productivity. Results: 107/146 (73%) of the scholars responded: 54% female, 59% Caucasian, 57% trainees and 61% trained in adult hematology (Table1). Responses to questions regarding the impact of the course on career choice and professional development indicated a strong positive impact of the course on scholars (Table 2). Current employment, participation in scholarly activities and productivity of former scholars demonstrated engagement in clinical research (65% of scholars >25% FTE in research), research in cellular therapy (89%), peer review (75%), and other academic activities. While scholars from the earlier cohort (2007-2012) had numerically more grants and publications and more senior academic appointments than the early cohort (2013-18), both cohorts were active in all productivity areas (Table 3). Conclusion: The ASTCT CRTC has positively contributed to retention of trainees and junior faculty in academic cellular therapy careers. The ASTCT should continue to support the CRTC and consider a second course to expand the opportunity to a larger number of scholars.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".