[Evaluation of the accessibility of stroke units in Poland].
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: According to the World Health Organization (WHO) assumptions every patient with a stroke should be treated by a specialized stroke team or in a specialized stroke unit. The aim of our study was to evaluate the accessibility and eligibility of stroke units in Poland. MATERIALS AND METHODS: We have developed a questionnaire evaluating the structure and staff of neurological departments. The questionnaire was sent to all neurological departments in Poland in the last quarter of 2003. We grouped the departments in 5 categories: having a stroke unit of class A (fulfilling the criteria of the Experts of the National Program of Prevention and Treatment of Stroke guidelines), class B (conditionally fulfilling the criteria), class C (not fulfilling the criteria), departments having stroke units without a separate structure and departments without stroke units. The classification was shown to each provincial consultant in order to verify our data in January 2004. RESULTS: We have received replies from 194 of 222 (87.4%) departments. 97 departments declared having a stroke unit: 20 -- class A, 56 -- class B, 14 -- class C and 7 -- stroke units without a separate structure. The remaining 97 departments do not have any stroke units. The most frequent problem that plays a role in the classification of departments was the deficit of staff or equipment. CONCLUSIONS: The stroke unit network in Poland is developing dynamically, but a significant number of the existing stroke units need more diagnostic equipment, staff and structural changes.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".