Tackling the radiotherapy (RT) shortage in Sub-Saharan Africa by gathering and using data from LMICs and HICs facilities for designing a future robust RT facility
Bibliographic record
Abstract
Purpose Historically, highly sophisticated medical linear accelerators (linacs) produced for high- (HIC) and upper-middle-income country (MIC) markets frequently experience significant additional operational failures in low- and lower-middle-income countries (LMICs). This study focuses on LMICs in Africa where there is a substantial equipment shortfall, projected to be a gap by 2040 of about 5000 linacs. The purpose of this study was to gain an insight into the poor performance of linac components, the unreliable infrastructure often encountered in LMICs and the consequent linac-related treatment downtime. Methods and Materials A questionnaire was sent to at least one cancer center in each of the 28 African countries that had experience treating cancer patients with linacs at the time of the survey (4 more countries have acquired linacs) since we completed this survey. For comparison questionnaires were sent to selected facilities in four high-income countries (Canada, Switzerland, UK, US) and to Jordan, a middle-income country. To investigate factors influencing linac downtime, we first utilised flow diagrams to illustrate the dependence of linac subsystem performance on infrastructural/environmental factors, the availability of spare parts and local repair capability. Secondly, a univariate analysis correlated linac downtime with factors such as method of linac fault diagnosis and staffing. Finally, a multivariate analysis investigated the relationship between GDP per capita and cancer mortality to incidence ratio statistics and compared these with the surveyed linac downtime across low-, middle- and high- income countries. Results Responses to the survey confirmed significant multi-factorial issues that influence the extent of linac downtime especially the performance of multi-leaf collimators, electron guns, vacuum systems, RF power and software. Other challenges include electrical power instability, inadequate national funding (GDP/capita), and workforce capability as well as a significant shortfall in formal education and training programmes for the radiation therapy (RT) workforce. Conclusion This survey identified numerous modes of radiotherapy (RT) equipment failure causing treatment downtime in LMICs that can be overcome by improvements in the design of RT technology but they need to be accompanied by increased RT staff training, improved broadband access and increased annual national funding for RT. The collaborative network of linac-based RT facilities in 28 1 * African countries that was developed to conduct this study is available for further investigations as RT capacity and capability improve in Africa.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".