Representation Matters: Expanding the Leadership Table for All Radiation Oncology Trainees
Bibliographic record
Abstract
Radiation oncologists are critical members of the cancer care continuum, providing treatment to more than 60% of individuals with cancer diagnoses.1 In the United States (US), the radiation oncology (RO) workforce is composed of approximately 5,500 physicians. However, stark underrepresentation from marginalized and excluded racial and ethnic communities2 is magnified throughout the workforce pipeline and has shown little change from a historic perspective. Despite a doubling of RO resident positions from 1974 to 2016, Black RO residents decreased from 5.9% to 3.2%, without much change in 2019, the year before the creation of the Association of Residents in Radiation Oncology (ARRO) Equity and Inclusion Subcommittee (EISC)2 (Fig. 1). Attrition of radiation oncologists from groups underrepresented in medicine (UIM) along the career ladder has subsequently resulted in a scarcity of representation in academic leadership positions.3 As founders of the ARRO EISC, we hope a deeper understanding of our process, guiding principles, challenges, and future goals provides a model to reimagine and redesign leadership at any stage in medical training.Fig. 1
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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.017 | 0.026 |
| Insufficient payload (model declined to judge) | 0.024 | 0.018 |
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".