Trends of ischaemic and haemorrhagic stroke hospitalizations in Hungary between 2010 and 2023: a nationwide, retrospective analysis of real-world data
Bibliographic record
Abstract
BACKGROUND: Adequate capacity planning of healthcare systems, ensuring effective and accessible care is key. Our aim was to examine the trends of ischaemic and haemorrhagic stroke-related hospitalizations in Hungary. METHODS: We conducted a nationwide retrospective analysis between 2010 and 2023. Data was provided by the Hungarian Central Statistical Office and the Pulvita Healthcare Data Warehouse. Crude patient and case numbers, number of inpatient care days, the mean length of (hospital) stay, hospital mortality rate, as well as crude and age-standardized hospitalization rate per 100,000 population were calculated for both men and women, and for both ischaemic stroke (IS) and haemorrhagic stroke (HS). RESULTS: In 2010, there were 224,379 cases of stroke in Hungary (94.31% IS, 5.69% HS), while in 2023 the number decreased to 152,649 cases (92.32% IS, 7.68% HS). The age-standardized hospitalization rate was 2644.97 per 100,000 people in 2010 and decreased to 1663.58 per 100,000 people by 2023. The mean length of hospital stay also decreased from 9.89 days to 8.89 days. HS mortality showed improvement in all age groups, especially among young children (0–4 years: -22.42%, 5–18 years: -10.36%). August consistently has a lower admission rate, while October and March show peaks. In terms of in-hospital mortality, the maximum rates are observed from January to March (10.74%, 10.07%, and 9.58%, respectively), as well as in December (8.84%), while June records the lowest mortality (7.95%). CONCLUSIONS: We draw attention to the importance of optimizing resources, especially considering our findings regarding seasonality. This underscores the necessity for more efficient allocation of capacities, equipment, and human resources. TRIAL REGISTRATION: Not applicable.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".