Geographic Accessibility to Clinical Trials for Head and Neck Cancers in the United States
Bibliographic record
Abstract
Head and neck cancers (HNCs) are becoming more common, thereby gaining greater attention within the medical community. This retrospective trend analysis examined geographical access to HNC clinical trials in the United States from 2005 to 2024, utilizing Census data and the Haversine formula. A search of ClinicalTrials.gov identified 23,450 trial sites, with 18,394 initiated before 2020. Although linear regression revealed a slight annual increase in trial initiation (37.947 trials/year, R² = 0.014, P = .625), most observed trends did not reach statistical significance. The proportion of the population residing within 1 mile of the trials saw a minor increase (0.328%/year, R² = 0.178, P = .064). Accessibility remained consistent throughout the COVID-19 pandemic, despite a decline in trial initiation during 2020. Enhancing access to trials, especially for marginalized populations, could improve patient engagement and clinical results.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".