Racial Diversity in Cancer Models: A Call to Action for Nanomedicine Researchers
Bibliographic record
Abstract
Global cancer incidence is projected to surge by 47% from 2020 to 2040, exacerbating existing healthcare disparities, particularly among ethnically diverse women. This article examines the urgent need for equitable therapeutic innovation in cancer care, focusing on the lack of diversity in preclinical cancer models. Our analysis of the top 50 cited papers on gynecological cancers in nanomedicine reveals an overreliance on cell lines predominantly of European origin, raising questions about the generalizability of findings. Using the Estimated Cell Line Ancestry database, we further explore the underrepresentation of equity deserving groups in available cancer cell lines while highlighting challenges and strategies that can be employed to address this growing issue.
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 | 0.090 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".