Indirect impact of the war in Ukraine on primary percutaneous coronary interventions for ST-elevation myocardial infarction in Poland
Bibliographic record
Abstract
INTRODUCTION: The Russian invasion of Ukraine in February 2022 resulted in displacement of approximately 12.5 million refugees to adjacent countries, including Poland, which may have strained health care service delivery. OBJECTIVES: Using the ST‑segment elevation myocardial infarction (STEMI) data, we aimed to evaluate whether the Russian invasion of Ukraine has indirectly impacted delivery of acute cardiovascular care in Poland. PATIENTS AND METHODS: We analyzed all adult patients undergoing percutaneous coronary interventions (PCIs) for STEMI across Poland between February 25, 2017 and May 24, 2022. The investigated health care centers were allocated to regions below and over 100 km from the Polish-Ukrainian border. Mixed‑effect generalized linear regression models with random effects per hospital were used to explore the associations between the war in Ukraine and several parameters, and whether these associations differed across the regions below and over 100 km from the border. RESULTS: A total of 90 115 procedures were included in the analysis. The average number of procedures per month was similar to the predicted volume for centers over 100 km from the border, while it was higher than expected (by an estimated median of 15 [interquartile range, 11-19]) for the region below 100 km from the border. There was no difference in adjusted fatality rate or quality of care outcomes for pre- and during‑war time in both regions, with no evidence of a difference‑in‑difference across the regions. CONCLUSIONS: Following the Russian invasion of Ukraine, there was only a modest and temporary increase in the number of primary PCIs, predominantly in the centers situated within 100 km of the Polish-Ukrainian border, although no significant impact on in‑hospital fatality rate was found.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".