The impact of cancer metastases on COVID‐19 outcomes: A COVID‐19 and Cancer Consortium registry‐based retrospective cohort study
Bibliographic record
Abstract
Abstract Background COVID‐19 can have a particularly detrimental effect on patients with cancer, but no studies to date have examined if the presence, or site, of metastatic cancer is related to COVID‐19 outcomes. Methods Using the COVID‐19 and Cancer Consortium (CCC19) registry, the authors identified 10,065 patients with COVID‐19 and cancer (2325 with and 7740 without metastasis at the time of COVID‐19 diagnosis). The primary ordinal outcome was COVID‐19 severity: not hospitalized, hospitalized but did not receive supplemental O2, hospitalized and received supplemental O2, admitted to an intensive care unit, received mechanical ventilation, or died from any cause. The authors used ordinal logistic regression models to compare COVID‐19 severity by presence and specific site of metastatic cancer. They used logistic regression models to assess 30‐day all‐cause mortality. Results Compared to patients without metastasis, patients with metastases have increased hospitalization rates (59% vs. 49%) and higher 30 day mortality (18% vs. 9%). Patients with metastasis to bone, lung, liver, lymph nodes, and brain have significantly higher COVID‐19 severity (adjusted odds ratios [ORs], 1.38, 1.59, 1.38, 1.00, and 2.21) compared to patients without metastases at those sites. Patients with metastasis to the lung have significantly higher odds of 30‐day mortality (adjusted OR, 1.53; 95% confidence interval, 1.17–2.00) when adjusting for COVID‐19 severity. Conclusions Patients with metastatic cancer, especially with metastasis to the brain, are more likely to have severe outcomes after COVID‐19 whereas patients with metastasis to the lung, compared to patients with cancer metastasis to other sites, have the highest 30‐day mortality after COVID‐19.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".