Helpful Hand of a Canadian Medical Team for the Severely Wounded Ukrainian Defenders
Bibliographic record
Abstract
Oleh M. Antonyshyn, MD, FRCS(C) is a Professor in the Division of Plastic Surgery (University of Toronto) with a subspecialty practice in craniomaxillofacial surgery. Dr. Antonyshyn established a Clinical Fellowship in Adult Craniofacial Surgery in 1993, providing post-residency specialized training in adult craniofacial surgery to candidates from Canada, the United States, Ireland, Israel and the Middle East. In 2008 he was awarded the A. Freiberg Plastic Surgery Resident Teaching Award. Also, Dr. Antonyshyn serves as: • Head, Adult Craniofacial Program (founded by Dr. Antonyshyn in 1996), Sunnybrook Hospital (Toronto, ON, Canada). • Affiliate Scientist, Physical Sciences, Trauma, Emergency & Critical Care Research Program, Sunnybrook Research Institute (Toronto, ON, Canada). • Full time clinical staff, Division of Plastic Surgery, Sunnybrook Health Sciences Centre (Toronto, ON, Canada). • Member, Global Advisory Board of the Advanced Cranio-Maxillo-Facial Forum. • Member, Examination Board in Plastic Surgery for the Royal College. The impact of contribution of Dr. Antonyshyn, his medical team (Fig), and a Canada-Ukrainian Foundation Mission is enormous. A lot of saved faces, given hope for a better quality of life due to novelty complex rehabilitation operations, multiple prosthesis and plenty of minor esthetic procedures for the Ukrainian defenders. The statistics of the Canadian team (doctors, medical assistants) from 2014 to 2018 is numerous, and keeps increasing. In the National Military Medical Clinical Center “Main Military Clinical Hospital” (Kyiv, Ukraine) together with the Ukrainian team of Maxillofacial, Neurocranial, Orthopedic, and Otorhinolaryngology Departments were performed 346 consultations and 239 surgeries to the severely wounded defenders of Ukraine. Predominant amount of patients (85 percent) consists of patients with post-traumatic defects, maxillofacial deformities, cranial, and limbs` injuries. So, the grateful words for Dr. Antonyshyn and Canadian team are endless.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.092 | 0.026 |
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".