A Mobile Craniofacial Surgery Unit: Reconstructing Casualties of War in Ukraine
Bibliographic record
Abstract
This paper describes the development and implementation of a mobile craniofacial surgical unit designed to address complex posttraumatic craniofacial deformities in both civilian and military casualties resulting from Russia's invasion of Ukraine. Restricted air space, limited possibilities for transportation of personnel and equipment, frequent interruption of power and water supply, and constant threat of injury to patients and medical personnel from missile and drone strikes, precludes reliable and safe delivery of tertiary care. The Canada Ukraine Surgical Aid Program (CUSAP) addressed these challenges by establishing a mobile craniofacial surgery unit, operating just outside of the war zone. The following report characterizes the civilian and military casualties, highlights the barriers to the provision of adequate tertiary care locally, and provides a detailed description of the measures that were taken to organize the mobile unit. The effectiveness of this program is documented, and specific challenges are illustrated through case examples. We believe this model serves as a template for delivering surgical aid to victims of any global disaster where care cannot be provided locally.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".