A systematic review of current national hospital-based stroke registries monitoring access to evidence-based care and patient outcomes
Bibliographic record
Abstract
BACKGROUND: National stroke clinical quality registries/audits support improvements in stroke care. In a 2016 systematic review, 28 registries were identified. Since 2016 there have been important advances in stroke care, including the development of thrombectomy services. Therefore, we sought to understand whether registries have evolved with these advances in care. The aim of this systematic review was to identify current, hospital-based national stroke registries/audits and describe variables (processes, outcome), methods, funding and governance). METHODS: We searched four databases (21st May 2015 to 1st February 2024), grey literature and stroke organisations' websites. Initially two reviewers screened each citation; when agreement was satisfactory, one of four reviewers screened each citation. The same process was applied to full texts. If there were no new publications from registries identified in the original 2016 review, we contacted the registry leads. We extracted data using predefined categories on country (including income level), clinical/process variables, methods, funding and governance. RESULTS: We found 37 registries from 31 countries (28 high income, four upper-middle income, five lower-middle income) of which 16 had been identified in 2016 and 21 were new. Twenty-two of the same variables were collected by >50% of registries/audits (mostly acute care, including thrombectomy, and secondary prevention), compared with only four variables in 2016. Descriptions of funding, management, methods of consent and data privacy, follow-up, feedback to hospitals, linkage to other datasets and alignment of variables with guidelines were variably reported. Reasons for apparent termination of some registries was unclear. CONCLUSIONS: The total number of stroke registries has increased since 2016, and the number of variables collected has increased, reflecting advances in stroke care. However, some registries appeared to have ceased; the reasons are unclear.
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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 | 0.035 | 0.178 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.023 | 0.028 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".