Global Connections: Telemedicine as a Tool to extend Trauma Education
Bibliographic record
Abstract
ABSTRACT Introduction Telemedicine is revolutionizing the delivery of trauma care and education. The International Trauma Tele- Grand Rounds is a series that unites institutions worldwide to discuss complex clinical cases and advanced trauma and critical care topics. Materials and methods Multiple remote institutions connect simultaneously to discuss the management of a trauma patient from the prehospital phase to discharge. Weekly, a case is presented in English by one institution on a rotating basis. Key points include mechanism of injury, resuscitation, laboratory and imaging diagnostics, surgical interventions, postoperative patient care, evaluation of treatment decisions and review of the literature. The highly interactive format allows participants to evaluate differences in trauma care across international health systems. Results During 2010 to 2011, 68 sessions were documented. Cases include blunt (42.6%), penetrating (48.5%), blast (7.4%) and crushing (1.5%) traumas. Gunshot wounds were the most frequent (25%). A holistic range of injuries were represented including injuries to major arteries, veins, lungs, heart, pericardium, esophagus, diaphragm, abdominal wall, stomach, intestines, liver, kidneys, pelvis, and the extremities. A variety of surgical and nonsurgical interventions were explored. To date, there have been 42 participating institutions from the United States, Brazil, Colombia, Bahamas, Canada, Mexico, Venezuela, Argentina, Panama, Puerto Rico, Dominican Republic, British Virgin Islands, Spain, Thailand, Turkey and Iraq; ranging from academic medical centers military hospitals, community hospitals, and rural hospitals. In 2011, the series received accreditation by the Accreditation Council for Continuing Medical Education. Conclusion Telemedicine offers a solution to address the disparities in access to trauma care and education. The diversity of institutional settings allows participants to learn from others on how to best treat trauma patients, despite differences in resources and expertise. In addition to serving as an educational tool, the series provides a mechanism for physicians to network and collaborate on future endeavors. How to cite this article Marttos AC, Kuchkarian FM, Rojas DF, Fraga GP, Collet-Silva FS, de Almeida Costa C, Garcia GD, Ginzburg E, Schulman CI, Namias N. Global Connections: Telemedicine as a Tool to extend Trauma Education. Panam J Trauma Critical Care Emerg Surg 2013;2(1):62-66.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".