Development of the stroke unit network in Poland--current status and future requirements.
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: According to the World Health Organization establishments and European Stroke Initiative recommendations, every patient with stroke should be treated by a specialized stroke team or in a specialized stroke unit. We aimed to evaluate the development of the stroke unit network in Poland and accessibility of stroke units in 2005. MATERIAL AND METHODS: Questionnaires evaluating structure and staff of neurological departments were sent to all neurological departments in Poland in the second quarter of 2005. We divided departments into the following categories: those having a class A stroke unit (fulfilling criteria of experts of the National Programme of Prevention and Treatment of Stroke) class B stroke unit (conditionally fulfilling those criteria), class C stroke units (not fulfilling the criteria), and departments without stroke units. The classification was presented to the chief consultant in neurology of each voivodship in December 2005 for verification of the data. RESULTS: We received enquiries from 180 out of 220 (81.8%) departments. Consultants included data of a further 8 departments which did not respond to the questionnaire (188 - 85.5%). 105 departments declared having a stroke unit (58 class A units, 40 class B units, and 7 class C units). 83 other departments do not have stroke units. The most frequent problem that plays a role in classification of departments was the deficit of staff or lack of equipment. It is assessed that the supplementation of diagnostic equipment and staff in existing stroke units and the establishment of 27 new stroke units is required to satisfy current needs. CONCLUSIONS: The development of the stroke unit network in Poland is proceeding dynamically. There are 105 stroke units in Poland but 45% of them require additional diagnostic equipment and staff.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".