Social Media Perceptions of Surgical Cancer Care in the Era of COVID-19: A Global Cross-Sectional Study
Bibliographic record
Abstract
PURPOSE: The rapid dissemination of information through social media renders a profound lens to evaluate perceptions of emerging topics, especially in the context of a global pandemic. The primary objective of this cross-sectional study was to elucidate trends on social media in the setting of surgical cancer care affected by the COVID-19 pandemic across the globe. METHODS: A public search of Twitter from April 1 to 30, 2020, was conducted, which yielded 996 posts related to COVID-19 and cancer. Two authors (E.J.K. and H.S.) individually reviewed all posts and recorded the post category, engagement, author category, and geographic location. Data were then analyzed through descriptive analyses. Only English-language posts were included, and any noncancer- or non-COVID-related posts were excluded from the analysis. RESULTS: A total of 734 unique authors from 26 different countries wrote 996 relevant posts that averaged 12.0 likes, 4.7 retweets, and 0.5 hashtags per post. Only 2.3% (23 of 996) of posts included a video. Authors of the included tweets most frequently were friends and families of patients (183; 18.4%), academic institutions or organizations (182; 18.3%), and physicians (138; 13.9%). Topics of importance were cancellations of surgeries (299; 40.1%), COVID-19 education (211; 121.2%), and research studies (93; 9.3%). The United Kingdom and the United States made up 81.5% of the cohort, followed by Canada (6.6%) and India (2.4%). Of posts where a specific type of surgery was identified (196), the most common type mentioned was breast cancer (50; 25.5%), followed by lung cancer (37; 18.9%) and urologic cancer (22; 11.2%). CONCLUSION: This analysis provides insight into the resulting impacts of COVID-19 on the global discussion of surgical cancer care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".