The Risk and Prognosis of COVID-19 Infection in Cancer Patients
Bibliographic record
Abstract
Numerous studies have been published regarding outcomes of cancer patients infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus causing the coronavirus disease 2019 (COVID-19) infection. However, most of these are single-center studies with a limited number of patients. To better assess the outcomes of this new infection in this subgroup of susceptible patients, we performed a systematic review and meta-analysis to evaluate the impact of COVID-19 infection on cancer patients. We performed a literature search using PubMed, Web of Science, and Scopus for studies that reported the risk of infection and complications of COVID-19 in cancer patients and retrieved 22 studies (1018 cancer patients). The analysis showed that the frequency of cancer among patients with confirmed COVID-19 was 2.1% (95% confidence interval [CI]: 1.3-3) in the overall cohort. These patients had a mortality of 21.1% (95% CI: 14.7-27.6), severe/critical disease rate of 45.4% (95% CI: 37.4-53.3), intensive care unit (ICU) admission rate of 14.5% (95% CI: 8.5-20.4), and mechanical ventilation rate of 11.7% (95% CI: 5.5-18). The double-arm analysis showed that cancer patients had a higher risk of mortality (odds ratio [OR]=3.23, 95% CI: 1.71-6.13), severe/critical disease (OR=3.91, 95% CI: 2.70-5.67), ICU admission (OR=3.10, 95% CI: 1.85-5.17), and mechanical ventilation (OR=4.86, 95% CI: 1.27-18.65) than non-cancer patients. Furthermore, cancer patients had significantly lower platelet levels and higher D-dimer levels, C-reactive protein levels, and prothrombin time. In conclusion, these results indicate that cancer patients are at a higher risk of COVID-19 infection-related complications. Therefore, cancer patients need diligent preventive care measures and aggressive surveillance for earlier detection of COVID-19 infection.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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".