Dissemination of Anesthesia Information During the Coronavirus Disease 2019 Pandemic Through Twitter: An Infodemiology Study
Bibliographic record
Abstract
BACKGROUND: Twitter is a web-based social media platform that allows instantaneous sharing of user-generated messages (tweets). We performed an infodemiology study of the coronavirus disease 2019 (COVID-19) Twitter conversation related to anesthesiology to describe how Twitter has been used during the pandemic and ways to optimize Twitter use by anesthesiologists. METHODS: This was a cross-sectional study of tweets related to the specialty of anesthesiology and COVID-19 tweeted between January 21 and October 13, 2020. A publicly available COVID-19 Twitter dataset was filtered for tweets meeting inclusion criteria (tweets including anesthesiology keywords). Using descriptive statistics, tweets were reviewed for tweet and account characteristics. Tweets were filtered for specific topics of interest likely to be impactful or informative to anesthesiologists of COVID-19 practice (airway management, personal protective equipment, ventilators, COVID testing, and pain management). Tweet activity was also summarized descriptively to show temporal profiles over the pandemic. RESULTS: Between January 21 and October 13, 2020, 23,270 of 241,732,881 tweets (0.01%) met inclusion criteria and were generated by 15,770 accounts. The majority (51.9%) of accounts were from the United States. Seven hundred forty-nine (4.8%) of all users self-reported as anesthesiologists. 33.8% of all tweets included at least one word or phrase preceded by the # symbol (hashtag), which functions as a label to search for all tweets including a specific hashtag, with the most frequently used being #anesthesia. About half (52.2%) of all tweets included at least one hyperlink, most frequently linked to other social media, news organizations, medical organizations, or scientific publications. The majority of tweets (67%) were not retweeted. COVID-19 anesthesia tweet activity started before the pandemic was declared. The trend of daily tweet activity was similar to, and preceded, the US daily death count by about 2 weeks. CONCLUSIONS: The toll of the pandemic has been reflected in the anesthesiology conversation on Twitter, representing 0.01% of all COVID-19 tweets. Daily tweet activity showed how the Twitter community used the platform to learn about important topics impacting anesthesiology practice during a global pandemic. Twitter is a relevant platform through which to communicate about anesthesiology topics, but further research is required to delineate its effectiveness, benefits, and limitations for anesthesiology discussions.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".