Impact of cancer diagnoses on the outcomes of patients with COVID-19: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The COVID-19 has caused significant mortality and morbidity across the globe. Patients with cancer are especially vulnerable given their immunocompromised state. We aimed to determine the proportion of COVID-19 patients with cancer, their severity and mortality outcomes through a systematic review and meta-analysis (MA). METHODS: Systematic review was performed through online databases, PubMed, Medline and Google Scholar, with keywords listed in the Methods section (1 November 2019-31 December 2020). Studies with clinical outcomes of at least 10 COVID-19 patients and at least one with a diagnosis of cancer were included. The studies for MA were assessed with PRISMA guidelines and appraised with Newcastle-Ottawa Scale. The data were pooled using a random-effects model using STATA software. The main outcomes were planned before data collection, including proportion of patients with cancer among COVID-19 populations, relative risk (RR) of severe outcomes and death of patients with cancer compared with general COVID-19 patients. RESULTS: =97.69%, p<0.001). The pooled RR of death was 1.44 (95% CI 1.19 to 1.76) between patients with cancer and the general population with COVID-19 infection. The pooled RR of severe outcome was 1.49 (95% CI 1.18 to 1.87) between cancer and general COVID-19 patients. The presence of lung cancer and stage IV cancer did not result in significantly increased RR of severe outcome. Among the available IPD, only age and gender were associated with severe outcomes. CONCLUSION: Patients with cancer were at a higher risk of severe and death outcomes from COVID-19 infection as compared with general COVID-19 populations. Limitations of this study include publication bias. A collaborative effort is required for a more complete database.
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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.043 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".