The clinical impact of COVID-19 on patients with cancer in British Columbia: An observational study
Bibliographic record
Abstract
Objective: We evaluated survival outcomes for patients with cancer and COVID-19 in this population-based study. Methods: A total of 631 patients who tested positive for severe acute respiratory syndrome coronavirus 2 and were seen at BC Cancer between 03/03/2020 and 01/21/2021 were included, of whom 506 had a diagnosis of cancer and PCR-confirmed positive test for coronavirus disease 2019. Patient clinical characteristics were retrospectively reviewed and the influence of demographic data, cancer diagnosis, comorbidities, and anticancer treatment(s) on survival following severe acute respiratory syndrome coronavirus 2 infection were analyzed. Results: Age ≥65 years (Hazard Ratio [HR] 4.77, 95% Confidence Interval [CI] 2.72-8.35, P < 0.0001), those with Eastern Cooperative Oncology Group Performance Status ≥2 (HR 8.36, 95% CI 2.89-24.16, P < 0.0001), hypertension (HR 3.17, 95% CI 1.77-5.66, P < 0.0001), and metastatic/advanced stage (HR 3.70, 95% CI 1.77-7.73, P < 0.0001) were associated with worse coronavirus disease 2019 specific survival outcomes following severe acute respiratory syndrome coronavirus 2 infection. Patients with lung cancer had the highest 30-day COVID-19 specific mortality (25.0%), followed by genitourinary (18.1%), gastrointestinal (16.0%), and other cancer types (<10.0%). Patients with the highest 30-day coronavirus disease 2019 specific mortality according to treatment type were those on chemotherapy (23.0%), rituximab (22.2%), and immunotherapy (16.7%) while patients on hormonal treatments (2.2%) had better survival outcomes (P = 0.041) compared to those on other anticancer treatments. Conclusion: This study provides further evidence that patients with cancer are at increased risk of mortality from coronavirus disease 2019 and emphasizes the need for vaccination.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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 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".