Abstract 21: Mapping Breast Cancer Resource Availability Across Ghana's Eastern Region
Bibliographic record
Abstract
Abstract Purpose: Improve allocation of breast cancer resources across Ghana's Eastern Region by systematically assessing and mapping breast cancer screening, diagnosis, and treatment capacity. Methods: A survey to assess breast cancer care capacity was developed and administered to hospitals in the Eastern Region of Ghana. Hospitals were stratified based on their available services. All facilities were mapped using GPS coordinates to demonstrate the location and distance between key services using thresholds of 10, 25, and 45km for screening, diagnostic, and surgical treatment capacity respectively. Results: Thirty-three (out of 34) hospitals in the Eastern Region were surveyed: 27 (82%) hospitals provide basic screening services (clinical diagnosis with clinical breast exam), 11 (33%) provide pathologic diagnosis, and 3 (9%) provide those services in addition to basic surgical care. A total of 53% of the population lives within a 10km travel distance of screening services, 64% within 25km of diagnostic services, and 78% within 45km of surgical treatment. Ninety-four percent of facilities have a general practitioner; 55% have a radiology technician; 39% employ a surgeon. Oncology specialists, and pathology personnel are only available in 1 facility across the Eastern Region. Limited chemotherapy is available at 2 hospitals, endocrine therapy at 1 hospital, and radiotherapy is not available. Conclusion: No hospitals are able to provide the full spectrum of care outlined by the Basic NCCN Framework Guidelines, which are intended for use in low resource settings. Although 82% of hospitals provide screening, only half the population lives within reasonable distance of these services. Few hospitals offer diagnosis and surgical treatment services. This geospatial analysis suggests two priorities in order to cost-effectively expand breast cancer services: increase the quantity of screening hospitals in targeted locations, and enhance treatment services at hospitals already providing diagnosis and surgery. Citation Format: Moustafa Moustafa, Meghan Mali, Fidel Lopez-Verdugo, Ousman Sanyang, Grace Ayensu-Danquah, Justin Sorensen, Jonathan Nellermoe, Stephen Manortey, Alison Goldsmith, Irina Ofei, Alberta Biritwurm-Nyarko, Raymond Price, Edward Sutherland. Mapping Breast Cancer Resource Availability Across Ghana's Eastern Region [abstract]. In: Proceedings of the 9th Annual Symposium on Global Cancer Research; Global Cancer Research and Control: Looking Back and Charting a Path Forward; 2021 Mar 10-11. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2021;30(7 Suppl):Abstract nr 21.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".