Impact of COVID-19 on patients receiving chemotherapy for gynecological cancer
Bibliographic record
Abstract
Background: Cancer patients’ increased susceptibility to serious COVID-19 complications can be attributed to the immunosuppressed state caused by the disease and anticancer treatments such as chemotherapy or surgery. Objectives: To assess the effect of COVID-19 pandemic on gynecological cancer patients receiving chemotherapy. Methods: A cross-sectional study was conducted on patients receiving chemotherapy for gynecological cancer between (March 2020 to February 2021) at King Abdulaziz University Hospital (KAUH) in Jeddah, Saudi Arabia. Clinical data collected from medical records included patients’ ages, medical history data, cycles of chemotherapy, COVID-19 infection, complications and death. Results: Total of 84 patients were identified. The mean age of studied patients was 53.81 ± 13.76 years, and the most common chronic diseases were HTN (35.7%) and DM (23.8%). The majority of diagnoses were ovarian cancer (41.7%) followed by uterine cancer (33.3%). Of studied patients, 17.9%, 19.1%, 27.4 and 33.3% had I, II , III and IV cancer stages respectively. The mean number of cycles of chemotherapy was 7.14 ± 5.55. 52.4% had first line chemotherapy. 57 percent of patients had delays due to various causes, including COVID-19 infection, and 9 percent of patients had COVID-19 while on therapy. 15 percent of the delays were caused by patients who were affected by Covid-19 while receiving chemotherapy and 2% of the patients died as a result of COVID-19 . Patients with recurrent disease had a significantly higher percentage of patients detected with COVID-19, and all cases detected with COVID-19 died with respiratory failure. Patients who had their chemotherapy delayed had a significantly higher mean number of cycles. Conclusion: Improved communication and management programs are required to keep cancer patients and their healthcare providers connected, as well as to allow cancer patients to survive a pandemic. Key words: Impact, COVID-19, patients, chemotherapy, Jeddah, Saudi Arabia.
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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 | 0.000 |
| 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 | 0.001 |
| 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".