Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to examine the correlation between Twitter mentions and the number of academic citations of radiation oncology articles. MATERIALS AND METHODS: We reviewed all 178 clinical manuscripts of the 2 most important radiation oncology journals and "Brachytherapy," and all clinical manuscripts relating to radiation oncology from the top 10 impact factor oncology journals, published between January and February 2018. We collected the record of citations utilizing Scopus and Google Scholar platforms and the number of times an article was tweeted about using the "Altmetric Bookmarklet." χ test was used to compare distributions between groups and the Pearson coefficient was used for correlations between the Twitter metrics and academic citations. RESULTS: Overall, 71% of all articles were tweeted about at least once. There was a significant correlation between the number of tweets and the number of citations in Google Scholar (r=0.55, P<0.001) and in Scopus (r=0.59, P<0.001). The 11% of articles with a prepublication Twitter "buzz" (defined as an article with ≥10 tweets before publication) had 3.6 times more citations in Scopus (mean: 14.8 vs. 4.2, P<0.001) and 2.9 times more citations in Google Scholar (17.8 vs. 6.0, P<0.001) when compared with papers with no "buzz." CONCLUSIONS: Presence on Twitter was correlated with the number of academic citations of an article in radiation oncology. This suggests that Twitter is being utilized by the oncology community as a platform to discuss and disseminate high impact scientific articles. The correlation between Twitter and increasing the number of citations of an article through larger dissemination and exposure requires further studies.
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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 | 0.003 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".