Streamlining Acute Stroke Processes and Data Collection: A Narrative Review
Bibliographic record
Abstract
(1) Background: Acute ischemic stroke treatment has been thoroughly studied to identify strategies to reduce treatment times. However, many centers still struggle to achieve fast treatment times. Additionally, studies primarily focus on larger, more advanced centers; yet, smaller centers often face longer treatment times. (2) Objectives: The aim of this study is to analyze the existing literature reviewing stroke treatment processes in primary and comprehensive stroke centers that investigated or reduced treatment times. The articles identified were categorized based on the focus areas and approaches used. (3) Results: Three main categories of improvements were identified in the literature: (1) standardization of processes, (2) resource management, and (3) data collection. Both primary and comprehensive stroke centers were able to reduce treatment times through standardization of the processes. However, challenges such as variations in hospital resources and difficulties incorporating data collection software into workflow were highlighted. Additionally, many strategies to optimize resources and data collection that can benefit primary stroke centers were only conducted in comprehensive stroke centers. (4) Conclusions: Many existing strategies to improve stroke treatment times, such as pre-notification and mass stroke team alerts, have been implemented in both primary and comprehensive stroke centers. However, tools such as simulation training are understudied in primary stroke centers and should be analyzed. Additionally, while data collection and feedback are recognized as crucial for process improvement, challenges persist in integrating consistent data collection methods into clinical workflow. Further development of easy-to-use software tailored to clinician needs can help improve stroke center capabilities to provide feedback and improve treatment processes.
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 | 0.032 | 0.163 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".