Surveying the Challenges to Improve Linear Accelerator-based Radiation Therapy in Africa: a Unique Collaborative Platform of All 28 African Countries Offering Such Treatment
Bibliographic record
Abstract
Radiation therapy is a critical component for curative and palliative treatment of cancer and is used in more than half of all patients with cancer. Yet there is a global shortage of access to this treatment, especially in Sub-Saharan Africa, where there is a shortage of technical staff as well as equipment. Linear accelerators (LINACs) offer state-of-the-art treatment, but this technology is expensive to acquire, operate and service, especially for low- and middle-income countries (LMICs), and often their harsh environment negatively affects the performance of LINACs, causing downtime. A global initiative was launched in 2016 to address the technology and system barriers to providing radiation therapy in LMICs through the development of a novel LINAC-based radiation therapy system designed for their challenging environments. As the LINAC prototype design phase progressed, it was recognised that additional information was needed from LMICs on the performance of LINAC components, on variables that may influence machine performance and their association, if any, with equipment downtime. Thus, a survey was developed to collect these data from all countries in Africa that have LINAC-based radiation therapy facilities. In order to understand the extent to which these performance factors are the same or different in high-income countries, facilities in Canada, Switzerland, the UK and the USA were invited to participate in the survey, as was Jordan, a middle-income country. Throughout this process, LMIC representatives have provided input on technology challenges in their respective countries. This report presents the method used to conduct this multilevel study of the macro- and microenvironments, the organisation of departments, the technology, the training and the service models that will provide input into the design of a LINAC prototype for a LINAC-based radiation therapy system that will improve access to radiation therapy and thus improve cancer treatment outcomes. It is important to note that new technology should be introduced in a contextual manner so as not to disrupt existing health systems inadvertently, especially with regards to existing staffing, infrastructure and socioeconomic issues. A detailed analysis of data is underway and will be presented in a follow-up report. Selected preliminary results of the study are the observation that LINAC-based facilities in LMICs experience downtime associated with failures in multileaf collimators and vacuum pumps, as well as power instability. Also, that there is a strong association of gross national product per capita with the number of LINACs per population.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".