Early mortality in patients with cancer and COVID-19 infection treated with immunotherapy
Bibliographic record
Abstract
Immunotherapy in the presence of COVID-19 infections raises concerns because of potential overlapping clinical complications and immune system enhancement. Further investigation is warranted to establish its safety and to improve clinical decisions. We conducted a retrospective cohort study using linked health administrative data from Ontario, Canada to assess 30-day mortality in patients with solid tumors who were treated with immunotherapy within 120 days before testing positive for COVID-19. A stepwise multivariable logistic regression model was used to identify clinical factors associated with 30-day mortality. Between January 2020 and April 2023, 281 patients tested positive for COVID-19 and were included in our study. The mean age was 68 (Standard Deviation: 10.3), 45% (127/281) were females and 58% (163/281) had lung cancer. 59% of patients (167/281) were treated with single agent immunotherapy, and almost 80% received at least one dose of COVID-19 vaccine. The 30-day mortality was 22% (63/281) and < 5% of patients were admitted to ICU or required ventilation. Factors associated with higher mortality were older age (Odds Ratio (OR) 1.60, 95% confidence interval (CI) 1.07–2.39), prior radiation therapy (OR 2.38, 95%CI 1.08–5.28), lower hemoglobin (< 10 g/dl) (OR 4.08, 95%CI 1.89–8.82) and higher leucocytes count (> 11,000/mm 3 ) (OR 3.63, 95%CI 1.55–8.52). Immunotherapy does not seem to increase the risk of 30-day mortality in patients with COVID-19 infections compared to published outcomes of patients with cancer and COVID-19. Mortality was associated with certain clinical characteristics that need to be carefully examined when prescribing immunotherapy during future comparable pandemics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".