National stroke registries for monitoring and improving the quality of hospital care: A systematic review
Bibliographic record
Abstract
BACKGROUND: Routine monitoring of the quality of stroke care is becoming increasingly important since patient outcomes could be improved with better access to proven treatments. It remains unclear how many countries have established a national registry for monitoring stroke care. AIMS: To describe the current status of national, hospital-based stroke registries that have a focus on monitoring access to evidence-based care and patient outcomes and to summarize the main features of these registries. SUMMARY OF REVIEW: We undertook a systematic search of the published literature to identify the registries that are considered in their country to represent a national standardized dataset for acute stroke care and outcomes. Our initial keyword search yielded 5002 potential papers, of which we included 316 publications representing 28 national stroke registries from 26 countries. Where reported, data were most commonly collected with a waiver of patient consent (70%). Most registries used web-based systems for data collection (57%) and 25% used data linkage. Few variables were measured consistently among the registries reflecting their different local priorities. Funding, resource requirements, and coverage also varied. CONCLUSIONS: This review provides an overview of the current use of national stroke registries, a description of their common features relevant to monitoring stroke care in hospitals. Formal registration and description of registries would facilitate better awareness of efforts in this field.
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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 | 0.021 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".