Hospitals under target: how Russian aggression is destroying Ukraine's medical institutions
Bibliographic record
Abstract
ABSTRACT. The article describes the attacks of the Russian Federation on Ukrainian medical institutions. As of the end of 2024, it is known that 1938 medical facilities in Ukraine were damaged. It provides a retrospective review of war crimes against doctors in wars that the aggressor waged earlier – in Chechnya, Georgia, Syria. The actions of the Russians after the annexation of Crimea in 2014 and the invasion of Donbas are described. The international legal levers are listed through which war criminals can be brought to justice. It gives examples of aggressive actions of the invaders in different years of the war against medical institutions in different cities of Ukraine – Trostianets, Chernihiv, Mariupol, Kyiv. Arguments are presented about the intentionality of the actions by the occupiers, which exclude randomness, and therefore, they are undoubted war crimes. It highlights the difficult working conditions of doctors in Kherson during the occupation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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 teacher head, 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".