The Thoracic Surgery Social Media Network: Early experience and lessons learned
Bibliographic record
Abstract
BACKGROUND: The Thoracic Surgery Social Media Network (TSSMN) is a social media collaborative formed in 2015 by The Annals of Thoracic Surgery and The Journal of Thoracic and Cardiovascular Surgery to bring social media attention to key publications from both journals and to highlight major accomplishments in the specialty. Our aim is to describe TSSMN's preliminary experience and lessons learned. METHODS: Twitter analytics was used to obtain information regarding the @TSSMN Twitter handle and #TSSMN hashtag. TweetChat and general hashtag #TSSMN analytics were measured using Symplur (Symplur LLC, Los Angeles, Calif). A TSSMN Tweeter App was created, and its use and downloads were analyzed. RESULTS: Hashtag #TSSMN has a total of 17,181 tweets, 2100 users, and 32,226,280 impressions, with peaks in tweeting activity corresponding to TweetChats. Thirteen 1-hour TweetChats drew a total of 489 participants, 5195 total tweets, and 17,297,708 total impressions. The top demographic category of TweetChat participants included Doctors (47%), Advocates/Supports (11%), and Unknown (10%), with 3% characterized as patients. The TSSMN Tweeter iTunes App (Apple, Cupertino, Calif) was downloaded 3319 times with global representation. A total of 859 articles were viewed through the App, with 450 articles from The Annals of Thoracic Surgery and 409 from The Journal of Thoracic and Cardiovascular Surgery. CONCLUSIONS: We demonstrate that TSSMN further enhances the ability for the journals to connect with their readership and the cardiothoracic community. Ongoing studies to correlate social media attention with article reads, article-level metrics, citations, and journal impact factor are eagerly awaited.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".