The hidden curve behind COVID-19 outbreak: the impact of delay in treatment initiation in cancer patients and how to mitigate the additional risk of dying – the head and neck cancer model
Bibliographic record
Abstract
Abstract Purpose: The rapid spread of the SARS-CoV-2 pandemic around the world caused most healthcare services to turn substantial attention to treatment of these patients and also to alter the structure of healthcare systems to address an infectious disease. As a result, many cancer patients had their treatment deferred during the pandemic, increasing the time to treatment initiation, the number of untreated patients (which will alter the dynamics of healthcare delivery in the post-pandemic era) and increasing their risk of death. Hence, we analyzed the impact on global cancer mortality considering the decline in oncology care during the COVID-19 outbreak using head and neck cancer, a known time-dependent disease, as a model. Methods: An online practical tool capable of predicting the risk of cancer patients dying due to the COVID-19 outbreak and also useful for mitigation strategies after the peak of the pandemic has been developed, based on a mathematical model. The scenarios were estimated by information of 15 oncological services worldwide, given a perspective from the five continents and also some simulations were conducted at world demographic data. Results: The model demonstrates that the more that cancer care was maintained during the outbreak and also the more it is increased during the mitigation period, the shorter will be the recovery, lessening the additional risk of dying due to time to treatment initiation. Conclusions: This impact of COVID-19 pandemic on cancer patients is inevitable, but it is possible to minimize it with an effort measured by the proposed model.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".