Outcomes and Risk Factors of Patients With COVID-19 and Cancer (ONCORONA): Findings from The Philippine CORONA Study
Bibliographic record
Abstract
Background A study conducted in China on patients with coronavirus disease 2019 (COVID-19) showed that cancer conferred a five times increased risk for needing intensive care admission and mortality; No data has yet been collected and published from the Philippines. Thus, the investigators conducted this substudy to determine the association of having a history of cancer with clinical outcomes among patients included in the Philippine CORONA Study. Methodology Multi-center, retrospective cohort design Results A total of 244 patients had a history of cancer, out of 10,881 COVID-19 hospital admissions. After adjusting for different confounding variables of interest, having cancer was significantly associated with a 75% increased odds of having severe/critical COVID-19 at nadir (CI 95% 1.32, 2.33; p < 0.001). After adjusting for different confounding variables of interest, having cancer was significantly associated with the following time-to-event outcomes: 72% increase in hazard of in-hospital mortality (CI 95% 1.37, 2.16; p < 0.001), 65% increase in hazard of respiratory failure (CI 95% 1.31, 2.08; p < 0.001), and 57% increase in hazard of being admitted to ICU (CI 95% 1.24, 1.97; p < 0.001). Conclusion A history of cancer conferred poorer clinical outcomes on adult, hospitalized COVID-19 patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".