Impact of COVID-19 outbreak on cancer immunotherapy in Italy: a survey of young oncologists
Bibliographic record
Abstract
Background The coronavirus disease 2019 (COVID-19) pandemic has overwhelmed the health systems worldwide. Data regarding the impact of COVID-19 on cancer patients (CPs) undergoing or candidate for immune checkpoint inhibitors (ICIs) are lacking. We depicted the practice and adaptations in the management of patients with solid tumors eligible or receiving ICIs during the COVID-19 pandemic, with a special focus on Campania region. Methods This survey (25 questions), promoted by the young section of SCITO (Società Campana di ImmunoTerapia Oncologica) Group, was circulated among Italian young oncologists practicing in regions variously affected by the pandemic: high (group 1), medium (group 2) and low (group 3) prevalence of SARS-CoV-2–positive patients. For Campania region, the physician responders were split into those working in cancer centers (CC), university hospitals (UH) and general hospitals (GH). Percentages of agreement, among High (H) versus Medium (M) and versus Low (L) group for Italy and among CC, UH and GH for Campania region, were compared by using Fisher’s exact tests for dichotomous answers and χ2 test for trends relative to the questions with 3 or more options. Results This is the first Italian study to investigate the COVID-19 impact on cancer immunotherapy, unique in its type and very clear in the results. The COVID-19 pandemic seemed not to affect the standard practice in the prescription and delivery of ICIs in Italy. Telemedicine was widely used. There was high consensus to interrupt immunotherapy in SARS-CoV-2–positive patients and to adopt ICIs with longer schedule interval. The majority of the responders tended not to delay the start of ICIs; there were no changes in supportive treatments, but some of the physicians opted for delaying surgeries (if part of patients’ planned treatment approach). The results from responders in Campania did not differ significantly from the national ones. Conclusion Our study highlights the efforts of Italian oncologists to maintain high standards of care for CPs treated with ICIs, regardless the regional prevalence of COVID-19, suggesting the adoption of similar solutions. Research on patients treated with ICIs and experiencing COVID-19 will clarify the safety profile to continue the treatments, thus informing on the most appropriate clinical conducts.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".