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?.
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».