Comparative Analysis of Radiotherapy Linear Accelerator Downtime and Failure Modes in the UK, Nigeria and Botswana
Bibliographic record
Abstract
The lack of radiotherapy linear accelerators (linacs) in low- and middle-income countries (LMICs) has been recognised as a major barrier to providing quality cancer care in these regions, together with a shortfall in the number of highly qualified personnel. It is expected that additional challenges will be faced in operating precise, high-technology radiotherapy equipment in these environments, and anecdotal evidence suggests that linacs have greater downtime and higher failure rates of components than their counterparts in high-income countries. To guide future developments, such as the design of a linac tailored for use in LMIC environments, it is important to take a data-driven approach to any re-engineering of the technology. However, no detailed statistical data on linac downtime and failure modes have been previously collected or presented in the literature. This work presents the first known comparative analysis of failure modes and downtime of current generation linacs in radiotherapy centres, with the aim of determining any correlations between linac environment and performance. Logbooks kept by radiotherapy personnel on the operation of their linac were obtained and analysed from centres in Oxford (UK), Abuja, Benin, Enugu, Lagos, Sokoto (Nigeria) and Gaborone (Botswana). By deconstructing the linac into 12 different subsystems, it was found that the vacuum subsystem only failed in the LMIC centres and the failure rate in an LMIC environment was more than twice as large in six of the 12 subsystems compared with the high-income country. Additionally, it was shown that despite accounting for only 3.4% of the total number of faults, linac faults that took more than 1 h to repair accounted for 74.6% of the total downtime. The results of this study inform future attempts to mitigate the problems affecting linacs in LMIC environments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".