Videoconferencing for Large Animal Trauma Experiments During COVID-19: A Cross-Continent Experience
Bibliographic record
Abstract
INTRODUCTION: COVID-19 shutdowns in many research facilities across North America impacted preclinical trauma-related research and development. Shutdown limited the speed and resources available for large animal experiments necessary for advancing medical devices and technologies. However, the pandemic led to the rapid adoption and expansion of videoconferencing in social circles, workplaces, and primary care health settings. Here, we describe the use of simple videoconferencing equipment to plan and carry out 3 total weeks of large animal experiments with a large, cross-continent, interdisciplinary team testing a novel technology in swine models of noncompressible intraabdominal hemorrhage and junctional hemorrhage. MATERIALS AND METHODS: Animal experiments using swine were scheduled over 3 weeks in February and March 2021 to take place in Toronto, Canada. All relevant animal protocols and COVID-19 site-specific risk assessments were completed and approved by the responsible institutional committees. Experiments were conducted by connecting 12 total research personnel from 3 sites by a simple video conferencing setup which included low-cost, high-definition webcams and standard smartphones streaming to Zoom. RESULTS: Video conferencing allowed for 3 weeks of trauma experiments to take place at the height of Toronto's third peak of COVID-19 cases. Up to 3 experiments were completed for models requiring 6 hours of monitoring, and up to 5 experiments were completed for models requiring 3 hours of monitoring. The large amount of digital data collected during these experiments was rapidly shared with our network of collaborators, who analyzed results and interpreted findings in real time. CONCLUSIONS: The system described in this paper has the potential to reduce costs of trauma animal model development and allow for rapid testing and implementation of life-saving devices in settings with limited onsite personnel as experienced during the COVID-19 pandemic.
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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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".