Organizational Factors Determining Access to Reperfusion Therapies in Ischemic Stroke-Systematic Literature Review
Bibliographic record
Abstract
BACKGROUND: After onset of acute ischemic stroke (AIS), there is a limited time window for delivering acute reperfusion therapies (ART) aiming to restore normal brain circulation. Despite its unequivocal benefits, the proportion of AIS patients receiving both types of ART, thrombolysis and thrombectomy, remains very low. The organization of a stroke care pathway is one of the main factors that determine timely access to ART. The knowledge on organizational factors influencing access to ART is sparce. Hence, we sought to systematize the existing data on the type and frequency of pre-hospital and in-hospital organizational factors that determine timely access to ART in patients with AIS. METHODOLOGY: Literature review on the frequency and type of organizational factors that determine access to ART after AIS. Pubmed and Scopus databases were the primary source of data. OpenGrey and Google Scholar were used for searching grey literature. Study quality analysis was based on the Newcastle-Ottawa Scale. RESULTS: A total of 128 studies were included. The main pre-hospital factors associated with delay or access to ART were medical emergency activation practices, pre-notification routines, ambulance use and existence of local/regional-specific strategies to mitigate the impact of geographic distance between patient locations and Stroke Unit (SU). The most common intra-hospital factors studied were specific location of SU and brain imaging room within the hospital, and the existence and promotion of specific stroke treatment protocols. Most frequent factors associated with increased access ART were periodic public education, promotion of hospital pre-notification and specific pre- and intra-hospital stroke pathways. In specific urban areas, mobile stroke units were found to be valid options to increase timely access to ART. CONCLUSIONS: Implementation of different organizational factors and strategies can reduce time delays and increase the number of AIS patients receiving ART, with most of them being replicable in any context, and some in only very specific contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".