Lessons from the Brazilian radiotherapy expansion plan: A project database study
Bibliographic record
Abstract
Background: The Radiotherapy Expansion Plan for Brazil's Unified Health System (PER-SUS) was an innovative program designed by the Ministry of Health in 2012 to provide improvements to the challenging problem of access to radiotherapy in the country. This study sought to analyze the execution and implementation of installations proposed by PER-SUS, and their capacity to address the problems of radiotherapy access in Brazil. Methods: From the first release (February 2015) until October 2021, all PER-SUS monthly progress reports were retrospectively analyzed. The beneficiary institutions, project location, project status, project type, dates of the progress on the stages, and reasons for cancellations or possible justifications for changing the status were collected. Brazilian geographic data, health care demands, and cancer incidences were correlated. Finally, we performed an Ishikawa diagram and 5W3H methodology, aiming to better understand the findings and to yield possible ways to improve the access to radiotherapy. Findings: After ten years, the PER-SUS project delivered nearly 50% of the planned implementation of radiotherapy equipment. There was a 17% growth in the national number of linear accelerators (LINACS) with PER-SUS, against a 32% increase in cancer incidence in Brazil in the same period. The following points were identified: a high rate of beneficiary exclusions reflecting inappropriate selection or inadequate planning; delays in execution related to bureaucratic obstacles and underestimation of the requirements (logistics/people); early closing of the equipment factory as a result of lack of project prioritization by the Government. Interpretation: Only about 50% of PER-SUS are being carried out. However, delays and exclusions of beneficiaries were observed. The dimension of the need for radiotherapy care in Brazil is greater than considered, and might not be fully attended by PER-SUS. Geographic, epidemiological, logistical, and economic variables could be reevaluated to allow better strategic planning and improvement proposals. PER-SUS could be optimized for the next decade, by involving all stakeholders' participation, alignment, and engagement. In the future, the States and regions with a higher LINAC shortage should be prioritized to improve RT access across the country. Considering the data and the initial project deadline, PER-SUS did not achieve the pre-established goals specified by the Brazilian Government. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".