Prioritizing gaps in stroke care: A two-round Delphi process
Bibliographic record
Abstract
BACKGROUND: Despite international recognition of stroke as a significant health priority, discrepancies persist between the target values for stroke quality measures and the actual values that are achieved in clinical practice, referred to as gaps. This study aimed to reach consensus among international experts on prioritizing gaps in stroke care. METHODS: A two-round Delphi process was conducted, surveying an international expert panel in the field of stroke care and cerebrovascular medicine, including patient representatives, healthcare professionals, researchers, policymakers, and medical directors. Experts scored the importance and required effort to close 13 gaps throughout the stroke care continuum and proposed potential solutions. Data were analyzed using descriptive statistics and qualitative analysis methods. RESULTS: In the first and second Delphi rounds, 35 and 30 experts participated, respectively. Expert consensus was reached on the high importance of closing 11 out of 13 gaps. Two out of 13 gaps were considered moderately important to close, with expert consensus for one of these two gaps. Expert consensus indicated that only one gap, related to the prevention of complications after stroke, requires moderate effort to close, whereas the others were considered to require high effort to close. Key focus areas for potential solutions included: "Care infrastructure," "Geographic disparities," "Interdisciplinary collaboration," and "Advocacy and funding." CONCLUSIONS: While closing gaps in stroke care primarily requires high effort and substantial resources, targeted interventions in the identified key focus areas may provide feasible and clinically meaningful improvements.
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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 | 0.194 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.003 | 0.005 |
| 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".