233 Uganda-based Survey of Challenges in Breast Cancer Detection in Low and Middle Income Countries
Bibliographic record
Abstract
OBJECTIVES/GOALS: Low and middle income countries (LMICs) face challenges in early detection of breast cancer resulting in high breast cancer mortality. This study serves to identify gaps and opportunities for innovators seeking to address problems in early detection of breast cancer in Uganda and other LMICs. METHODS/STUDY POPULATION: Two methods were used: 1) Three weeks of ethnographic research in Uganda through primary stakeholder interviews and clinical observations. Interviews were conducted with patients, clinicians, NGOs, and key opinion leaders from the Uganda Cancer Institute, Makerere University, and JHPIEGO. Clinical observations were performed to note the workflow and availability of resources across diverse health centers ranging from village health teams in rural settings to the national referral hospital in the urban center. 2) A targeted literature search focused on breast cancer detection in LMICs. Keywords included breast cancer’, screening’, and diagnosis’. Identified challenges were validated through stakeholder interviews and categorized. Potential solutions to each challenge were explored. RESULTS/ANTICIPATED RESULTS: Three broad categories of challenges and suggested innovation targets were identified. 1) Ineffective clinical processes: deskilling and improving training around the process of clinical breast examinations, imaging operation and interpretation, and pathology preparation and interpretation; 2) Accessibility: increasing screening throughput, improving rural community access to breast cancer care, and increasing opportunistic screening; 3) Sensitization: increasing patient and health worker awareness of clinical presentations of breast cancer, reducing cultural barriers, and improving trust in the medical community. DISCUSSION/SIGNIFICANCE: Innovators seeking to solve problems in early breast cancer detection in LMICs should focus on ineffective clinical processes, accessibility, and sensitization. In conjunction with prompt treatment, there is potential to reduce breast cancer mortality rates in line with the Global Breast Initiative.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".