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Record W3184860510 · doi:10.2196/27073

Suggested Modifications to the Management of Patients With Breast Cancer During the COVID-19 Pandemic: Web-Based Survey Study

2021· article· en· W3184860510 on OpenAlexvenueno aff
Shereef Elsamany, Mohamed Elbaiomy, Ahmed A. Zeeneldin, Emad Tashkandi, Fayza Hassanin, Nafisa Abdelhafeez, Humaid O. Al‐Shamsi, Nedal Bukhari, Omima Elemam

Bibliographic record

VenueJMIR Cancer · 2021
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBreast cancerRadiation therapyCancerDiseaseInternal medicineOncologyHormonal therapyMetastatic breast cancerPandemicCoronavirus disease 2019 (COVID-19)Infectious disease (medical specialty)

Abstract

fetched live from OpenAlex

BACKGROUND: Management of patients with cancer in the current era of the COVID-19 pandemic poses a significant challenge to health care systems. Breast cancer is the most common cancer internationally. Breast cancer is a disease that involves surgery, chemotherapy, hormonal therapy, targeted therapy, radiotherapy, and, more recently, immunotherapy in its management plan. The immune system requires months to recover from these medications, and this condition is even worse in patients with metastatic breast cancer who need ongoing treatment with these drugs. Some of these drugs, such as inhibitors of cyclin-dependent kinases 4 and 6, can cause rare but life-threating lung inflammation. Patients with breast cancer who have metastatic disease to the lungs can experience deterioration of disease symptoms with COVID-19 infection. Oncologists treating patients with breast cancer are facing a difficult situation regarding treatment choice. The impact that COVID-19 has had on breast cancer care is unknown, including how to provide the best care possible without compromising patient and community safety. OBJECTIVE: The aim of this study was to explore the views of oncologists regarding the management of patients with breast cancer during the COVID-19 pandemic. METHODS: A web-based SurveyMonkey questionnaire was submitted to licensed oncologists involved in breast cancer management in Saudi Arabia, Egypt, and United Arab Emirates. The survey focused on characteristics of the participants, infection risk among patients with cancer, and possible treatment modifications related to different types of breast cancer. RESULTS: The survey was completed by 82 participants. For early hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancer, 61 of the 82 participants (74%) supported using neoadjuvant hormonal therapy in selected patients, and 58% (48/82) preferred giving 6 over 8 cycles of adjuvant chemotherapy when indicated. Only 43% (35/82) preferred inhibitors of cyclin-dependent kinases 4 and 6 with hormonal therapy as the first-line treatment in all patients with metastatic HR-positive disease. A total of 55 of the 82 participants (67%) supported using adjuvant trastuzumab for 6 instead of 12 months in selected patients with HER2-positive breast cancer. For metastatic HER2-positive, HR-positive breast cancer, 80% of participants (66/82) supported the use of hormonal therapy with dual anti-HER2 blockade in selected patients. The preferred choice of first-line treatment in metastatic triple negative patients with BRCA mutation and programmed cell death 1 ligand 1 (PD-L1) <1% was poly(adenosine diphosphate-ribose) polymerase inhibitor according to 41% (34/82) of the participants, and atezolizumab with nab-paclitaxel was preferred for PD-L1 >1% according to 71% (58/82) of the participants. CONCLUSIONS: Several modifications in breast cancer management were supported by the survey participants. These modifications need to be discussed on a local basis, taking into account the local infrastructure and available resources.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.048
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

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

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 head0.094
GPT teacher head0.419
Teacher spread0.326 · 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 teacher head, 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

Citations3
Published2021
Admission routes1
Has abstractyes

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