MétaCan
Menu
← Back to cohort

Patients' perspectives and safety of COVID-19 vaccination among cancer patients: A prospective single-center study.

2022· article· en· W4286299175 on OpenAlexaff
Nathalie Daaboul, Mélina Boutin, Catherine Sperlich, Margit Fuchs, Louis‐Patrick Haraoui, Giovanna Speranza, Nghia T Trung Nguyen, Flávia De Angelis, Samuel Martel, Sara V. Soldera, Sabrina Trudel, Line Srour, Benoit Samson, Susan Fox, Céline Devaux, Catherine Prady

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsHôpital Charles-Le MoyneUniversité de Sherbrooke
Fundersnot available
KeywordsMedicineAdverse effectInternal medicineVaccinationTolerabilityLung cancerCancerCommon Terminology Criteria for Adverse EventsPopulationMalignancyConfidence intervalImmunology

Abstract

fetched live from OpenAlex

e24043 Background: Concerns about safety and treatment interference are known barriers to COVID-19 vaccination in cancer patients. Data on safety and tolerability in this population remain scarce. One of the objectives of this study is to describe COVID-19 vaccination safety in cancer patients. Methods: Patients diagnosed with a malignancy requiring systemic treatment in the last 12 months and undergoing COVID-19 vaccination were prospectively enrolled in this single-center study. Validated questionnaires to assess vaccine-related adverse events (VRAEs) were collected; chart review identified baseline characteristics and treatments received. Descriptive statistics and logistic regressions were performed. Results: 253 questionnaires were collected from 171 patients, enrolled between May and September 2021. 130 patients were survey-eligible after the 1st dose (D1) and 185 after 2nd dose (D2). 91 questionnaires were collected after D1 (Questionnaire 1: Q1) and 162 after D2 (Questionnaire 2: Q2). Surveys couldn’t be collected due to interval > 1 month between D1 / enrollment, patients’ unavailability, withdrawal of study or death. Median age was 55 (24-87) and 62.8% were female. 58.5% had solid tumors, treated with chemotherapy (49%) or checkpoint inhibitors only (9.5%); 19.4% malignancies were treated with targeted therapies and 22.1% had hematological malignancies. Most frequent solid tumors were breast (31.3%), lung (15.9%) and gastro-intestinal (GI) (14.3%). Patients received 45.6% Pfizer/BioNTech, 52.8% Moderna and 1.6% Oxford/AstraZeneca. A combination of 2 different vaccines was administered to 11.9%. Interval between D1 and D2 was ≤30 days in 53.1%, 31-90 days in 42.6%, and 91-180 days in 4.3%. Among all patients, 84.1% developed VRAEs after a median of 2 days post-vaccine for a median of 4 days. 74.5% had local symptoms (Sx) (pain, sensitivity and/or redness at injection site and/or arm) and 65.8% had systemic Sx. Most frequent systemic Sx were fatigue, chills or myalgia (39.4%), GI (6.3%) and fever (2.9%). Most patients (90.7%) described their Sx as having no / minimal impact (Gr 1), 7.8% reported seeking medical consultation (Gr 2), and 1.5% lead to hospitalization (Gr 3) (1 cardiovascular event, 1 infection; causality with concurrent systemic treatment not excluded and 1 due to malignancy). Gr 2, but not Gr 3, VRAEs were more common after D2 (11.4% vs 2.5%, p = 0.03). 41.7% considered their Sx as a new health problem. On multivariate analysis, younger age and female sex were significantly associated with the development of any Sx (OR 1.08, p = 0.01; OR 2.92, p = 0.02, respectively) and local Sx (OR 1.04, p = 0.04; OR 2.19, p = 0.04), but not systemic Sx or new health problem. Conclusions: Patients experienced mostly minor and transient symptoms post-vaccination; few perceived these as a new health problem. COVID-19 vaccination is overall safe and well-tolerated among cancer patients.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.060
GPT teacher head0.432
Teacher spread0.372 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical Oncology→Same topicCancer Immunotherapy and Biomarkers→French-language works237,207→