Abstract D008: Resolving disparities in cancer patient outcomes in Trinidad and Tobago
Bibliographic record
Abstract
Abstract The Caribbean Cancer Research Initiative (CCRI) began as a result of the alarming rates of various cancer types in the Caribbean and the lack of data and credible sources of information on the prevalence of cancer in the region. What we do know is that Trinidad and Tobago has one of the highest cancer mortality rates in the Caribbean. Some explanations for this disparity can be lack of treatment innovation, educational gaps regarding screening, shortcomings in health care administration and cultural biases. Preliminary data indicates that Trinidad has one of the highest breast cancer mortality rates in the Americas. The five year breast cancer survival rate is at approximately 30% compared to over 90% in the US. The median time to diagnostic resolution based on preliminary research is 65 days, approximately 3 times what is it in US and Canada. CCRI’s mission is to build the cancer research capacity in the Caribbean starting in Trinidad which will aid in preventing more cancers and improving patient outcomes in the Caribbean. One of the main programs of the organization is the patient navigation service which is designed to meet the needs of persons diagnosed with cancer and their family members. This service has highlighted some of the contributing factors to cancer disparities in Trinidad. Examples of these that will be featured on this poster are socioeconomic factors, cultural phenomenon, geographical factors and poor access to proper care. The Caribbean Cancer Research Initiative is committed to assist patients to overcome their barriers to gain better outcomes, which will aid in resolving disparities in cancer health in Trinidad. Citation Format: Nalisha Monroe. Resolving disparities in cancer patient outcomes in Trinidad and Tobago [abstract]. In: Proceedings of the Twelfth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2019 Sep 20-23; San Francisco, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl_2):Abstract nr D008.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 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".