Will academic and community physicians engage and share knowledge in an online physician social network? Lessons from the Radiation Oncology Community
Bibliographic record
Abstract
e18200 Background: The exponential growth of medical knowledge has made it increasingly difficult for clinicians to make sense of new information and recognize how to incorporate new research results into clinical practice. We sought to determine whether a social question and answer website designed to connect academic and community physicians would consistently engage radiation oncologists (RO) to share evidence based information and clinical insights with each other. Methods: theMednet was initially piloted in radiation oncology and, after 2 years, opened to medical oncologists. Questions were posted via an ‘Ask Question’ button and answers were solicited from ROs involved in research relevant to the questions being asked. Engagement was measured in question clicks, monthly and quarterly logins, and searches. Answers were analyzed for quality by reference sources. Results: A total of 1696 US radiation oncologists registered to theMednet from 10/2013 to 1/2015. Users included community ROs (61%), academic ROs (22.6%), and residents (16.4%). Every quarter, 70% of ROs returned to the site and 42% returned monthly. Five hundred sixty-six questions were posted by 150 ROs, of which 497 were approved and 93% were answered. A total of 756 answers were viewed 81,125 times by 1,680 ROs. Answers were posted by 175 ROs from 111 institutions. Academics answered 97% of these questions and asked 8%. In a sample of 547 answers, 58% cited data, including a total of 508 publications. Personal insights were cited in 70% of the answers, and 42% cited both personal insights and published data. Conclusions: Academic and community radiation oncologists will engage regularly through an online social network when the conversations provide value to clinical practice, discuss published data, and share personal insights. Academics focus their efforts on answering questions, while community physicians are more likely to ask questions, showing a flow of knowledge from academic to community physicians. Future directions are focused on expanding to all oncology disciplines and introducing more customized educational content.
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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 | 0.019 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".