Point-of-care resuscitation research: From extreme to mainstream: Trauma Association of Canada Fraser Gurd Lecture 2019
Bibliographic record
Abstract
The Gurd Family surgical legacy was deeply intertwined with National service in both World Wars. My own personal research mission has attempted to emulate such service, by enhancing the tools and techniques available to facilitate point-of-care diagnosis and resuscitation in extreme and adverse environments. Our efforts involving point-of-care diagnosis/resuscitation and the telementored guidance of those remotely responding to catastrophic injury have included collaborations with National Aeronautics and Space Administration (NASA) the Canadian Space Agency, the Canadian Forces, its democratic allies, and non-governmental surgical organizations. Research has been conducted in resuscitative suites and operating theaters, research laboratories, parabolic flight aircraft, on humanitarian surgical missions, and from ski-hills and firehalls. The initial phases of these efforts involved inaugural studies in resuscitative sonography including defining the Extended Focused Assessment with Sonography for Trauma (EFAST) examination. Although the original work was commissioned for Space Medicine, generalization to mainstream practice further justifies space medicine research. Iterative steps in advancing telementored resuscitation have subsequently involved the maturation of space-mandated telementored ultrasound support (TMUS), exploration of TMUS in terrestrial clinical practice, and the creation of increasingly mobile (hand-held) TMUS solutions. Subsequently it was recognized that teleultrasound is simply one informatic dimension of remote telemedicine, and current efforts are focused in a Program known as TeleMentored Ultrasound Supported Medical Interactions (TMUSMI) of remote responders required to intervene with catastrophic trauma. While this research program has yielded many techniques and findings that have benefited mainstream terrestrial practice, these investigations are currently ongoing, and we hope to demonstrate that TMUSMI may benefit all Canadians especially those in remote areas, as well as potentially every global inhabitant without immediate access to care. Further, we propose that to fully utilize these techniques, a new specialty, that of the remote medical mentor will be required, a new specialty that will require the creation and scientific validation of its principles and techniques.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".