Effects of the COVID-19 pandemic on delays in diagnosis-to-treatment initiation for breast cancer in Brazil: a nationwide study
Bibliographic record
Abstract
Background: Short period from diagnosis to breast cancer (BC) treatment initiation remains challenging for the public health system in Brazil, which may have been further affected by the coronavirus disease-2019 (COVID-19) pandemic.This study assessed BC diagnosis-to-treatment intervals (DTi) in Brazil and the possible effects of the COVID-19 outbreak on delays.Methods: The Painel de Monitoramento de Tratamento Oncológico database was queried to obtain the number of Brazilian patients with a BC confirmed diagnosis and initiating cancer treatment in the pre- COVID-19 (2013COVID-19 ( -2019) ) and during the COVID-19 (2020-2021) periods, adopting a 60-day limit as timely treatment.A p-value of <0.05 was considered significant.Results: A total of 315,951 cases were included (females: 99.3% and males: 0.7%), of which 251,667 and 64,284 records were computed before and during the COVID-19 years, respectively.Most patients failed to perform the first cancer treatment within 60 days (>60: 51.8%).We observed an upward trend in the number of BC treatments provided in the pre-COVID-19 years (r² = 0.9575; p < 0.05), but the volume of treatments exhibited an average reduction of 24.6% yearly during the COVID-19 pandemic.The average DTi in days was 122.4,122.5 and 122.3 in the total period studied, before and during the COVID-19 outbreak, respectively.The arrival of COVID-19 in Brazil increased the chances of treatment delay (OR = 1.043; p < 0.05) and inverted the proportion of early/advanced stages at BC diagnosis (55.8%/44.2%-48.4%/51.6%).Conclusion: COVID-19 has imposed changes in BC care in Brazil, reducing the number of treatments provided by the Brazilian public health system, increasing the chances of delayed treatment initiation despite no differences in DTi averages being identified, and raising the proportion of advanced-stage diagnoses.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".