Global Emergency Medicine Journal Club: A Social Media Discussion About the Age-Adjusted D-Dimer Cutoff Levels to Rule Out Pulmonary Embolism Trial
Bibliographic record
Abstract
STUDY OBJECTIVE: Annals of Emergency Medicine collaborated with an educational Web site, Academic Life in Emergency Medicine (ALiEM), to host an online discussion session featuring the 2014 Journal of the American Medical Association publication on the Age-Adjusted D-Dimer Cutoff Levels to Rule Out Pulmonary Embolism (ADJUST-PE) trial by Righini et al. The objective is to describe a 14-day (August 25 to September 7, 2014) worldwide academic dialogue among clinicians in regard to 4 preselected questions about the age-adjusted D-dimer cutoff to detect pulmonary embolism. METHODS: Five online facilitators hosted the multimodal discussion on the ALiEM Web site, Twitter, and Google Hangout. Comments across the social media platforms were curated for this report, as framed by the 4 preselected questions, and engagement was tracked through various Web analytic tools. RESULTS: Blog and Twitter comments, as well as video expert commentary involving the ADJUST-PE trial, are summarized. The dialogue resulted in 1,169 page views from 391 cities in 52 countries on the ALiEM Web site, 502,485 Twitter impressions, and 159 views of the video interview with experts. A postdiscussion summary on the Journal Jam podcast resulted in 3,962 downloads in its first week of publication during September 16 to 23, 2014. CONCLUSION: Common themes that arose in the multimodal discussions included the heterogeneity of practices, D-dimer assays, provider knowledge about these assays, and prevalence rates in different areas of the world. This educational approach using social media technologies demonstrates a free, asynchronous means to engage a worldwide audience in scholarly discourse.
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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 | 0.024 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".