Lives and Economic Loss in Brazil Due to Lack of Radiotherapy Access in Cervical Cancer
Bibliographic record
Abstract
Background: Cervical cancer collects the highest survival benefit from radiotherapy (RT) among all malignancies. A large gap between oncological demand and RT availability exists for cervical cancer in Brazilian Public Health System (BPHS). Aim: To evaluate cost-effectiveness of universal access to RT and chemo-radiation (CRT) for untreated cervical cancer patients in the BPHS. Methods: The incremental cost was calculated based on the direct medical cost from a payer's perspective and the proportion of new cases with unmet RT/CRT needs in 2016. The incremental effectiveness was evaluated by life-year (LY) gain based on life expectancy, cervical cancer incidence and the number of cancer deaths due to lack of RT/CRT access as previously described. The incremental cost-effectiveness ratio (ICER) was calculated from direct medical costs and LYs. The indirect costs from mortality-related productivity loss (MRPL) were estimated based on life expectancy, wage and labor force participation rate. The MRPL was compared with direct medical cost. All costs and effectiveness were age-adjusted based on 2016 Brazilian data and discounted at 3% per year. Costs were adjusted to 2016 U.S. dollars. One-way sensitivity analysis was performed to assess the robustness of the model. Results: The annual cost to close RT gap was $14.3 million, with additional cost of $4.1 million to close the CRT gap. The average years of potential life lost per death was 20.5. Cost per life saved was $10,820 for RT alone (ICER: $528/LY) and $18,919 for CRT (ICER: $584/LY), respectively. The MRPL due to shortage of RT/CRT were 70/81 million respectively. Conclusion: Providing universal access to RT/CRT for cervical cancer patients in the BPHS will incur low cost per life-year saved and provide large economical gain by saving thousands of lives.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".