Optimizing Patient-Centered Stroke Care and Research in the Prehospital Setting
Bibliographic record
Abstract
Over the past decades, continuous technological advances and the availability of novel therapies have enabled treatment of more acute medical conditions than ever before. Many of these treatments, such as intravenous thrombolysis and mechanical thrombectomy for acute ischemic stroke, are highly time sensitive. This has raised interest in shifting advanced acute care from hospitals to the prehospital setting. Key objectives of advanced prehospital stroke care may include (1) early targeted treatments in the prehospital setting, for example, intravenous thrombolysis for acute stroke, and (2) advanced prehospital diagnostics such as prehospital large vessel occlusion and intracranial hemorrhage detection, to help inform patient triage and potentially reduce subsequent workload in emergency departments. Major challenges that may hamper a swift transition to more advanced prehospital care are related to conducting clinical trials in the prehospital setting to provide sufficient evidence for emergency interventions, as well as ambulance design, infrastructure, emergency medical service personnel training and workload, and cost barriers. Utilizing new technologies such as telemedicine, mobile stroke units and portable diagnostic devices, customized software applications, and smart storage space management may help surmount these challenges and establish efficient, targeted care strategies that are achievable in the prehospital setting. In this article, we delineate the paradigm of shifting advanced stroke care to the prehospital setting and outline future directions in providing evidence-based, patient-centered prehospital care. While we use acute stroke as an illustrative example, these principles are not limited to stroke patients and can be applied to prehospital triage for any time-critical disease.
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 | 0.055 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".