Impact of COVID-19 on the curative treatment of prostate cancer: a national cross-sectional study
Bibliographic record
Abstract
Background: COVID-19 pandemic affected access to cancer treatment worldwide. However, there is a lack of data about the impact in developing countries. The objective was to evaluate COVID-19 impact on curative prostate cancer (Pca) treatment in Brazil. Materials and methods: With data extracted from the Brazilian Ministry of Health database, the Non-COVID and COVID periods were analyzed to compare the absolute number of radical prostatectomy (RP) and radiotherapy (RT) executed in the country and regions. Results: With data from 50,169 Pca patients (NO COVID = 28,106 cases and COVID =22,063) treated with RP or RT in Brazil, a significant decline in patients receiving RT or RP (–6.043 cases; p = 0.0001) was detected. Both treatment procedures (RT or PR) were reduced in all five Brazilian regions comparing the Non-COVID and COVID periods. Overall, there was a reduction on RP and RT procedures in 92% (24/25) and 76% (19/25) of the evaluated states, respectively. Comparing the variation of RT and RP per state between COVID and Non-COVID period, there is a significant difference (–18.6% vs. –29%, p = 0.03) with a higher negative impact on the RP group. The RT and RP variation had no significant relationship with the incidence of COVID cases in the states. Limitations include the non-evaluation of treatment combinations, the impact of hypofractionated radiotherapy, and other factors influencing the treatment choice. Conclusions: During the COVID-19 pandemic, the curative treatment with RP and RT of Pca was abruptly limited and affected. However, the number of RP was more impacted than RT during the COVID period.
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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 | 0.002 |
| 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) | 0.002 | 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 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".