Uncovering the impact of COVID-19 on the place of death of cancer patients in South America
Bibliographic record
Abstract
The COVID-19 pandemic has significantly impacted healthcare systems worldwide, especially on the management of chronic diseases such as cancer. This study explores the effects of COVID-19 on cancer mortality trends in Brazil, Chile, and Peru. The monthly age-standardized mortality rates in different places of death (hospital/clinic or home) were estimated using vital statistics and death certificate databases. An interrupted time series analysis was performed for each country, using the date of lockdown implementation as the intervention point. Overall cancer mortality rates reduced after the implementation of pandemic restrictions, with a significant decrease in Brazil. In total, 75.3%, 55.4%, and 45.7% of deaths in Brazil, Peru, and Chile, respectively, occurred in hospitals. After lockdowns were implemented, at-home deaths increased in all countries, and in-hospital deaths correspondingly decreased only in Chile. Our results suggest that COVID-19 has significantly affected rates of cancer mortality and place of death in Latin America.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".