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Adherence and perception of the importance of anti-COVID-19 protective measures amongst patients with cancer.

2022· article· en· W4286298930 on OpenAlexaff
Aline Fusco Fares, Rafael Soleman Maritan, Vinicius Fernando Ribeiro, Gabriela Modulo Molina, Rafael Teixeira Barbosa, Victor Jun Ohya Imai, Rafael Augusto Modenez Mota, Joao Victor Moraes de Oliveira, Júlia Belone Lopes, Tatiana Elias Colombo, Daniel Vilarim Araújo

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsUniversity Health NetworkPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineCancerAsymptomaticPopulationInternal medicineCohortBreast cancerDiseaseEnvironmental health

Abstract

fetched live from OpenAlex

e24120 Background: Patients (pts) with cancer are at a higher risk of COVID-19 (C19) severe disease. However, our group and others have demonstrated a low prevalence of C19 infection among asymptomatic pts with cancer on active systemic treatment. We hypothesized this phenomenon was due to increased adherence of pts with cancer to anti-C19 protective measures. This study compares adherence and the perceived importance of such measures in oncologic and non-oncologic populations. Methods: This is a cross-sectional study conducted between June and September 2021. A questionnaire was developed to assess how participants perceived and adhered to masking, handwashing, and other anti-C19 protective measures. Demographic information, educational level, and monthly income were also collected. Three groups were included: A) pts with cancer treated at Hospital de Base (HB); B) pts without cancer treated at HB for other medical conditions; C) a healthy outpatient population. Data were summarized in means, medians, and proportions. Chi-squared or Fisher´s exact test was used to compare categories; ANOVA was employed to compare means. A multivariable analysis assessing factors associated with adherence to anti-C19 protective measures was performed. Results: A total of 607 people were interrogated (200 in groups A and B, and 207 in C). The mean age of groups A, B, C was significantly different: 60 vs. 52 vs. 35 years old (p < 0.001). Group C had a higher educational level and a higher monthly income than groups A and B (p < 0.001). In cohort A, gastrointestinal, breast, and genitourinary were the most frequent tumors (33%, 31%, 15%); 75% of pts had active disease. Pts with cancer were more adherent to anti-C19 protective measures: of 33 points, the mean score of groups A, B, C was respectively 27.8 vs. 25.5 vs. 21.9 (p < 0.001). Regarding the importance of anti-C19 protective measures, pts with cancer also perceived those as more relevant: of 21 points, the mean score of groups A, B, C was respectively 17.8 vs. 17 vs. 16.9 (p < 0.001). For instance, in group A, 95% report consistently wearing a mask when leaving home compared to 90% and 58.2% of groups B and C. In line with our findings, 20.5% vs. 32.5% vs. 35.3% (p < 0.003) of groups A, B, and C reported having C19 before the questionnaire. In multivariable analysis, groups B and C were less likely to adhere to anti-C19 protective measures than group A. Conclusions: Pts with cancer are more adherent to anti-C19 protective measures and perceive them as more important than non-oncologic populations. Our results shed light on the accumulating literature of a low incidence of C19 amongst asymptomatic pts with cancer on systemic treatment even during the surges. Such findings are possibly related to the fact that cancer pts are more vulnerable to hospitalizations and unfavorable outcomes than the general population, prompting a more risk-averse behavior.

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)0.005
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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.199
GPT teacher head0.511
Teacher spread0.313 · 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".

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Citations1
Published2022
Admission routes1
Has abstractyes

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