Racial and ethnic disparities in US neuroendocrine tumor clinical trial enrollment over the past quarter century
Bibliographic record
Abstract
e16353 Background: Neuroendocrine tumors (NETs) are a heterogeneous group of neoplasms originating from neuroendocrine cells, with considerable variation in behavior and clinical presentation. The rising incidence of NETs has driven advancements in drug development; however, it remains unclear whether all demographic groups have equal access to novel therapeutic trials. Previous oncologic epidemiological studies have identified race-specific disparities in overall and disease-specific survival. This study aimed to assess whether similar racial and ethnic disparities exist in the enrollment of patients in NET clinical trials. Methods: We collected data from all completed NET clinical trials involving adult patients conducted in the United States over the past 25 years (1/1/2000 to 1/1/2025) as reported on clinicaltrials.gov. Therapeutic interventional trials were included while observational, non-interventional or non-therapeutic trials were excluded. Key study variables included race, ethnicity, sex, tumor type, and year of study. To assess enrollment patterns, we calculated the enrollment ratio for each racial and ethnic group, defined as the percentage of enrollees from each group divided by their proportion among the overall NET patient population. Results: We analyzed 64 NET trials, comprising 5,020 participants total. The gender distribution was similar, with 2,455 women and 2,565 men. Racial demographic data were reported in 60.9% (39/64) of the trials, while ethnicity data were provided in 42.2% (27/64). The reporting of race increased markedly, rising from 16.7% during 2008–2011 to 78.3% in 2020–2024. The comparison of enrollment ratios revealed that Black participants were significantly underrepresented compared to White participants (0.292, p < 0.001 ), and Hispanic participants were significantly underrepresented compared to Non-Hispanic participants (0.536, p < 0.001 ). Conclusions: Black and Hispanic Americans are significantly underrepresented in NET clinical trials. Clinical trials are critical for developing effective treatments and understanding how interventions perform across populations. The lack of representation hinders our ability to evaluate how NET therapies affect different racial and ethnic groups, potentially worsening existing health disparities. Enhancing the inclusion of underrepresented minorities in NET clinical trials is vital to promoting equitable care and improving health outcomes for all patients. Demographic representation and enrollment ratios in NETs clinical trials. Black White Hispanic Non-Hispanic NETs Patient Demographics 16.1% 74.0% 10.4% 89.6% NETs Clinical Trial Enrollee Demographics 5.33% 83.5% 5.69% 91.3% Enrollment Ratio 0.331 1.13 0.546 1.02 Relative Enrollment Compared with White 0.292 (p<0.001) 1.00 - - Relative Enrollment Compared with Non-Hispanic - - 0.536 (p<0.001) 1.00
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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 | 0.019 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".