COAGS Trauma Update on Ukraine Initiatives
Bibliographic record
Abstract
In this presentation, Markian Pahuta discusses the ongoing challenges faced by Ukrainian orthopedic surgeons amid the ongoing conflict in Ukraine. He emphasizes that war disproportionately impacts orthopedic surgery, with many civilians suffering significant trauma. The Ukrainian healthcare system, already weakened prior to the war, faces additional strain from attacks on healthcare facilities and an overwhelming number of civilian casualties. Pahuta introduces the ASSIST project from McMaster University, which aims to support Ukrainian surgeons through education, virtual training, and logistics for surgical supplies. He highlights collaboration between Canadian and Ukrainian medical professionals to share knowledge and resources, including live-streamed surgeries and translated educational materials. The project has thus far raised over $2 million in donations for surgical supplies, improving the quality of resources available to Ukrainian surgeons. Pahuta concludes with an appeal for continued support and collaboration, noting a future focus on involving infectious disease specialists to address complications arising from severe injuries. The session calls for community involvement and donations to further enhance the capabilities of medical professionals in Ukraine.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.035 |
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".