Analysis of Thrombolysis Process for Acute Ischemic Stroke in Urban and Rural Hospitals in Nova Scotia Canada
Bibliographic record
Abstract
Background: Stroke is a devastating disease, but it is treatable with alteplase or tissue plasminogen activator (tPA). The effectiveness of tPA is highly time-dependent, meaning rapid treatment is critical. Fast treatment with tPA has been reported in many urban hospitals, but hospitals in rural locations struggle to reduce treatment times. This qualitative study examines current thrombolysis processes in one urban and two rural hospitals in Nova Scotia, Canada, by mapping and comparing the treatment process in these settings for acute ischemic stroke (AIS) patients, and by analyzing the healthcare professionals views on various treatment topics. Methods: Structured interviews were conducted with healthcare professionals involved in stroke treatment across the three sites. The interviews focused on the various activities in the thrombolysis treatment at each site. Additionally, participants were asked about the following 10 topics: comfort treating acute ischemic stroke patients; perceptions about tPA; appropriate tPA treatment window; stroke patient priority; tPA availability; patient consent; urban-rural treatment differences; efficiency of their treatment process; treatment delays; and suggested process improvements. Results were analyzed using the Framework Method, as well as through the development of process maps. Results: Twenty three healthcare professionals were interviewed at 2 rural hospitals and 1 urban hospital. Acute ischemic stroke patients are triaged as the highest or urgent priority at each included site. Physicians are more hesitant to treat with tPA in rural settings. A total of 11 urban-rural treatment differences were noted by the rural sites. Additionally, 11 patient-related and 29 system treatment delays were described. A process map was developed for each site, representing the arrival by ambulance and by private vehicle pathways. Conclusions: Guidelines and clear protocols are critical in reducing treatment times and ensuring consistent access to treatment. The majority of treatment delays encountered are system delays, which can be appropriately planned for to reduce delays within the care pathway. There is a general consensus that there is an urban-rural treatment gap for acute ischemic stroke patients in Nova Scotia, and that continuing education is key in rural hospitals to improve Emergency Department (ED) physician comfort with treating patients with tPA.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".