Associations between the use of Twitter as a technology platform in oncology and the scientific impact of its users.
Bibliographic record
Abstract
16 Background: Social media channels, such as Twitter, represent relatively new technology platforms for scientific users to disseminate research findings and communicate their views and interpretations to colleagues and followers. To date, the associations between the use of Twitter and the scientific impact of its users are unclear. Methods: All Canadian oncologists who are full members of the American Society of Clinical Oncology were identified from the online membership directory. Users of Twitter were defined as those with an active Twitter account, as of June 2019, and posted at least one tweet within the past year. Data regarding the number of tweets, likes, and followers were collected by an online search of Twitter. Scientific impact of each individual was assessed based on a user’s h-index and number of citations from Google Scholar as well as score from Research Gate. Associations were examined with summary statistics and correlation coefficients. Results: We identified 676 eligible oncologists of whom 80 (12%) and 596 (88%) currently use and do not use Twitter. Among the users, the median number (IQR) of tweets, likes, and followers were 196 (45-865), 325 (86-1,246), and 198 (89-449), respectively. The scientific impact of Twitter users versus non-users was statistically similar (see Table). Likewise, within the group of users, there was no correlation between the number of tweets, likes, and followers and the scientific impact of individuals (correlation coefficients 0.38, 0.34, and 0.41, respectively, all p > 0.05). Conclusions: Only 1 in 10 oncologists use Twitter, but those who use Twitter leveraged this technology platform frequently. There was no association between the use of Twitter and the scientific impact of its users. Views from a minority of oncologists are represented on Twitter. Such bias underscores the need to exercise caution when using social media for scientific knowledge exchange. Regular evaluations of new technologies are warranted to ensure the quality and rigor of their scientific content. [Table: see text]
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".