An exploratory analysis of pediatric anesthesia activity on Twitter using the #pedsanes hashtag
Bibliographic record
Abstract
BACKGROUND: The use of social media within the medical field has rapidly evolved over the past two decades, with Twitter being one of the most common platforms of engagement. The use of hashtags such as #pedsanes has been reported as a community builder around the subject of pediatric anesthesia. Understanding the use of #pedsanes can inform dissemination of pediatric anesthesia content and discourse. We aimed to describe the distribution and patterns of tweets and contributors using #pedsanes across the globe. METHODS: Using Tweetbinder (https://www.tweetbinder.com) and the R package "academictwitteR," we extracted tweets that included the hashtag "#pedsanes" from March 14, 2016 to March 10, 2022. Tweets were analyzed for frequency, type, unique users, impact and reach, language, content, and the most common themes. RESULTS: A total of 58 724 tweets were retrieved; 22 071 (38.8%) were original tweets including 3247 replies, while 35 971 (61.2%) were retweets all generated by over 5946 contributors located in at least 122 countries. The frequency distribution of tweets gradually increased over time with peaks in activity corresponding to major pediatric anesthesia societal meetings and during the early phases of the COVID-19 pandemic. The most retweeted and most liked posts included images. DISCUSSION: We report the widespread and increasing use of social media and the "#pedsanes" hashtag within the pediatric anesthesia and medical community over time. It remains unknown the extent to which Twitter hashtag activity translates to changes in clinical practice. However, the #pedsanes hashtag appears to play a key role in disseminating pediatric anesthesia information globally.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".