A Practical Approach to the Management of Cancer Patients During the Novel Coronavirus Disease 2019 (COVID-19) Pandemic: An International Collaborative Group
Bibliographic record
Abstract
The outbreak of coronavirus disease 2019 (COVID-19) has rapidly spread globally since being identified as a public health emergency of major international concern and has now been declared a pandemic by the World Health Organization (WHO). In December 2019, an outbreak of atypical pneumonia, known as COVID-19, was identified in Wuhan, China. The newly identified zoonotic coronavirus, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is characterized by rapid human-to-human transmission. Many cancer patients frequently visit the hospital for treatment and disease surveillance. They may be immunocompromised due to the underlying malignancy or anticancer therapy and are at higher risk of developing infections. Several factors increase the risk of infection, and cancer patients commonly have multiple risk factors. Cancer patients appear to have an estimated twofold increased risk of contracting SARS-CoV-2 than the general population. With the WHO declaring the novel coronavirus outbreak a pandemic, there is an urgent need to address the impact of such a pandemic on cancer patients. This include changes to resource allocation, clinical care, and the consent process during a pandemic. Currently and due to limited data, there are no international guidelines to address the management of cancer patients in any infectious pandemic. In this review, the potential challenges associated with managing cancer patients during the COVID-19 infection pandemic will be addressed, with suggestions of some practical approaches. IMPLICATIONS FOR PRACTICE: The main management strategies for treating cancer patients during the COVID-19 epidemic include clear communication and education about hand hygiene, infection control measures, high-risk exposure, and the signs and symptoms of COVID-19. Consideration of risk and benefit for active intervention in the cancer population must be individualized. Postponing elective surgery or adjuvant chemotherapy for cancer patients with low risk of progression should be considered on a case-by-case basis. Minimizing outpatient visits can help to mitigate exposure and possible further transmission. Telemedicine may be used to support patients to minimize number of visits and risk of exposure. More research is needed to better understand SARS-CoV-2 virology and epidemiology.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".