Prognosis of COVID-19 in patients with breast cancer
Bibliographic record
Abstract
BACKGROUND: Coronavirus disease 2019 (COVID-19) has become a pandemic in the world and posed a great threat to people's health. Several meta-analyses have indicated that many comorbidities were associated with increased risk of COVID-19 severity or mortality. The original report also showed that the mortality rate of COVID-19 in breast cancer patients is more dependent on comorbidities than previous radiation therapy or current anti-cancer therapy. However, no meta-analysis has focused on this aspect. This systematic review aims to assess whether breast cancer will increase the severity and mortality of patients infected with COVID-19 and to explore which factors that may affect the severity or mortality rate of breast cancer patients with COVID-19. METHODS: We will search the PubMed, Embase, Web of Science, the Cochrane Central Register of Controlled Trials (CENTRAL), China National Knowledge Infrastructure (CNKI), Chinese Biomedical Literature Database (CBM), and Wanfang database from December 1, 2019 to June 30, 2020. Cohort studies comparing the disease severity and mortality of COVID-19 patients with and without breast cancer will be included. Two independent reviewers will assess the risk of bias of the included cohort studies using the modified Newcastle-Ottawa Scale. We will conduct meta-analyses to calculate the risk ratio (RR) and 95% confidence interval (95% CI) using the random-effects model with the Mantel-Haenszel method. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach will be used to rate the quality of the evidence. RESULTS: The results of this study will be published in a peer-reviewed journal. CONCLUSION: This study will provide comprehensive evidence for medical staff to adopt effective treatment strategies for breast cancer patients during the COVID-19 pandemic. PROSPERO REGISTRATION NUMBER: CRD42020188208.
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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".