ESMO Management and treatment adapted recommendations in the COVID-19 era: Lung cancer
Bibliographic record
Abstract
The COVID-19 pandemic, characterised by a fast and global spread during the first months of 2020, has prompted the development of a structured set of recommendations for cancer care management, to maintain the highest possible standards. Within this framework, it is crucial to ensure no disruption to essential oncological services and guarantee the optimal care.This is a structured proposal for the management of lung cancer, comprising three levels of priorities, namely: tier 1 (high priority), tier 2 (medium priority) and tier 3 (low priority)-defined according to the criteria of the Cancer Care Ontario, Huntsman Cancer Institute and Magnitude of Clinical Benefit Scale.The manuscript emphasises the impact of the COVID-19 pandemic on lung cancer care and reconsiders all steps from diagnosis, staging and treatment.These recommendations should, therefore, serve as guidance for prioritising the different aspects of cancer care to mitigate the possible negative impact of the COVID-19 pandemic on the management of our patients.As the situation is rapidly evolving, practical actions are required to guarantee the best patients' treatment while protecting and respecting their rights, safety and well-being. In this environment, cancer practitioners have great responsibilities: provide timely, appropriate, compassionate and justified cancer care, while protecting themselves and their patients from being infected with COVID-19. In case of shortages, resources must be distributed fairly. Consequently, the following recommendations can be applied with significant nuances, depending on the time and location for their use, considering variable constraints imposed to the health systems. An exceptional flexibility is required from cancer caregivers.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".