How did the COVID-19 pandemic affect the management of patients with cancer? What is the impact of anti-cancer medications on the immune response to the COVID-19 vaccinations?.
Bibliographic record
Abstract
e24193 Background: This study had two objectives: To assess the impact of the COVID-19 pandemic on cancer patients’ management. To determine the impact of cancer types and anti-cancer medications on the immune response to COVID-19 vaccinations. Methods: Cancer patients at the Moncton Hospital, Oncology/Hematology Clinic, were invited to voluntarily complete a survey about the date of their cancer diagnosis, treatment, the impact of the COVID-19 pandemic on their health, and to complete a prepared list of possible side effects that they experienced after receiving any of the COVID-19 vaccines. Participants, who had received at least two doses of any COVID-19 vaccine, were given the option to consent to a sub-study to provide a blood sample to check their antibody response to the COVID-19 vaccination. Blood samples were collected at least 4 weeks after the second dose of a primary vaccine series, or at least 2 weeks after any booster dose. Samples were sent to the Dr. Georges-L.-Dumont University Hospital lab to be analyzed. Data was collected regarding the type of treatment (chemotherapy, immunotherapy, targeted therapy, hormonal therapy, radiation therapy, or a combination of these therapies). Analysis of the data was done using SPSS program. Results: 178 patients completed the survey. 117 patients had solid tumors, and 61 had a hematological malignancy. The mean age was 65.75 (range 31-90) years and 45.5% were males. 62.3% either had a high school diploma or a graduate degree. When asked if their anxiety about their health increased due to the pandemic, 57.8% reported that their anxiety level has moderately or somewhat increased. Similarly, around 70% responded that their stress levels moderately or somewhat increased due to the pandemic. Only 7.3% of patients mentioned that they had delays in diagnosis, and only 2.8% had changes in their treatment, however, around 30% had their appointments changed into phone appointments as a result of the pandemic. Only 4% reported a loss of job due to the pandemic, and 11.2% reported financial stress. Cross tabulations were done between type of cancer and results of the COVID-19 antibody test and type of treatment and results of the COVID-19 antibody test. Results showed that the type of treatment that the patient was receiving has no impact on the production of COVID-19 antibodies. However, antibodies after COVID-19 vaccination were not detected in 20.5% (10/49) of patients with hematological malignancies compared to 3.6% (3/82) of patients with solid tumors. This difference was statistically significant (p = 0.001). Conclusions: The pandemic affected Cancer patients in terms of increasing their anxiety and stress levels. The production of antibodies after COVID-19 vaccinations was significantly decreased in patients with hematological malignancies.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".