Abstract PS7-17: Exploring global clinical trial enrollment opportunities in high breast cancer mortality regions
Bibliographic record
Abstract
Abstract BACKGROUND Globally, breast cancer is the most commonly diagnosed cancer and the most common cause of cancer death for women. Access to high quality treatment and novel therapies vary significantly based on geographic regions and countries. Mortality rate is considerably higher women in developing and low-income regions compared to high income regions. While many factors contribute to disparities in breast cancer specific outcomes, access to clinical trials provide high quality and evidence-based cancer care. Therefore, we evaluated the availability of breast cancer treatment clinical trial among global regions with the highest breast cancer incidence and related mortality. METHODS In this study, we reviewed clinical trials registered with clinicaltrials.gov and published in 3 high impact journals: The New England Journal of Medicine, Lancet Oncology and the Journal of Clinical Oncology between January 2018 and May 2020. For each trial, the countries from which patients enrolled were captured and compared to the countries in the top 5 regions for highest breast cancer incidence and breast cancer related mortality globally per the GLOBOCAN 2018 estimates. RESULTS A total of 77 clinical trials meet this criteria and enrolled patients in 67 different countries. As most trials enrolled in multiple countries, the enrollment countries for every trial was recorded with a total of 697 enrolled countries in these 77 clinical trials. The global regions with the high breast cancer mortality are Melanesia, Micronesia and Polynesia (Hawaii excluded), Northern Africa, Caribbean and Western Africa. These regions together had only 8 clinical trials that accrued patients during this time frame: one trial in Egypt, 4 in New Zealand and 3 in Puerto Rico. This represents about 1% of country accruals in the trials analyzed (8 of 697). The most frequently enrolling countries were the United States, Canada, Belgium, United Kingdom, Italy, France, Germany, and Spain which have high incidence of breast cancer but lower mortality rates compared to other global regions. CONCLUSIONS High impact breast cancer trials currently enroll patients from areas with high incidence of breast cancer but not from areas with high mortality rates. Barriers that lead to these disparities have not been extensively explored and would be of interest in future studies. Exploring creative ways to bridge this gap, such as working with local governments to help develop clinical centers with ability to run clinical trials and train local medical staff, may be part of an effort to decrease global breast cancer mortality. Citation Format: Jean-Pierre A Blaize, Phillip P Acosta, Isiah Gonzalez, Kristen E Ott, Kate Lathrop. Exploring global clinical trial enrollment opportunities in high breast cancer mortality regions [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS7-17.
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 | 0.334 | 0.430 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".