First accounting of comprehensive radiotherapy life cycle assessment components in Africa.
Bibliographic record
Abstract
e23213 Background: Climate change is a pressing issue on the global stage. Recently a comprehensive lifecycle assessment (LCA) of external beam radiotherapy (EBRT) for cancer delineated the environmental and secondary health impacts of radiotherapy in the United States (US) (PMID 38821084). The continent of Africa is warming faster than any other in the world, leaving Africa to face the most disproportionate burden worldwide arising from climate change. Thus far, an LCA of EBRT has yet to be performed in Africa. We report initial pilot data on breast cancer patients treated in Africa with EBRT as a first step towards LCA analysis. As breast cancer is the most common indication for EBRT (and the most common cancer worldwide in women), it represents an optimal disease site to initiate LCA analysis. These findings represent the first assessment of the complete components of an LCA in Africa, using our experience from a Ghanaian hospital. Methods: Data collection was performed using the ISO 14040 and 14044 standards as a guide in accordance with PMID 38821084. The scope of the study was defined as one round of curative intent EBRT from initial consultation through delivery of the last fraction. LCA data components were comprised from breast cancer patients receiving adjuvant EBRT at Korle-Bu Teaching Hospital (KBTH), Ghana from 2021-2024. Data for a complete life cycle of adjuvant EBRT for breast cancer consisted of medical supplies, equipment, patient and staff travel, and building energy usage. Results: Ten breast cancer patients were assessed for data collection, of which six received 50 Gray (Gy) in 25 fractions; the remaining four received 40.05 Gy/15 fractions. Patients received EBRT via a cobalt machine (n = 9) or linear accelerator (n = 1). Medical supplies were grouped into reusable and single use items. For initial consultation, patients traveled median 13.9 km (8.6 mi), and median distance traveled by staff was 10.5 km (6.5 mi). CT simulation was used for planning; peer review and weekly on-treatment visits were performed by a Radiation Oncologist while pre-treatment quality assurance was completed by a medical physicist. During treatment, patients traveled median 15.3 km (9.5 mi), and radiation therapists traveled a median of 11.8 km (7.3 mi). Most associated with the radiation delivery process used public transit for travel. Clinic energy usage was in accordance with previously reported data (PMID: 37552912). Conclusions: Given the importance of radiotherapy in the treatment of cancer (involved in half of all cancers treated), an accurate LCA analysis of EBRT is essential for combating climate change worldwide. This study represents the first comprehensive accumulation of LCA data for the continent of Africa. Further analysis will involve assessment of these parameters to create an LCA which will have far reaching impact not only in breast cancer but in other disease sites both in Africa and worldwide.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".