The impact of the COVID-19 pandemic on cancer screening
Bibliographic record
Abstract
To the Editor, We read with interest the Letter to the Editor by Mungmunpuntipamtip and Wiwanitkit concerning our paper, where they report high cervical cancer screening and cancer detection rates in their settings during the COVID-19 pandemic. This is in contrast with previous studies from the USA (Miller et al., 2021), Canada (Walker et al., 2021), Zimbabwe (Murewanhema, 2021) and West Cameroon (Sormani et al., 2021) that found substantial decreases in cervical cancer screening rates. More recently, an online survey by the International Cancer Screening Network concluded that the first wave of the pandemic had a substantial impact on cancer screening worldwide, with the suspension of services in over 90% of the settings analyzed (Puricelli Perin et al., 2021). The effects of the COVID-19 pandemic led to service delays, health and broader infrastructure constraints, even in settings where services continued. In particular, there was a shift in public health priorities to respond to the pandemic, and health system restrictions occurred with shortages in materials for screening as supply chains were impacted. Furthermore, patients may have experienced barriers to access, as well as an unwillingness to attend screening due to fear of contracting COVID-19. Nevertheless, the magnitude of the effect of the pandemic is expectedly heterogeneous across settings, and ongoing monitoring of the impact of the pandemic on cancer screening and the management of backlogs must continue, as well as determining how service disruptions will translate to clinically meaningful changes in incidence, stage at diagnosis and poorer outcomes. Acknowledgements This study was funded by the Foundation for Science and Technology – FCT (Portuguese Ministry of Science, Technology and Higher Education) in collaboration with the Agency for Clinical Research and Biomedical Innovation (AICIB), under the scope of the project 'Impacto da pandemia COVID-19 nos cuidados prestados a doentes oncológicos' (Research 4 COVID 174_596850546), and national funding from FCT, under the Unidade de Investigação em Epidemiologia – Instituto de Saúde Pública da Universidade do Porto (EPIUnit; UIDB/04750/2020). SM was also funded under the scope of the project 'NEON-PC - Neuro-oncological complications of prostate cancer: longitudinal study of cognitive decline' (POCI-01-0145-FEDER-032358; ref. PTDC/SAU-EPI/32358/2017), which is funded by FEDER through the Operational Programme Competitiveness and Internationalization, and national funding from FCT. The funding sources had no involvement in the conduct of the research and/or preparation of the article. Conflicts of interest There are no conflicts of interest.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.003 |
| 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".