Developing NWO Navigate: An App for Navigating Stroke Care
Bibliographic record
Abstract
Abstract BackgroundA coordinated system of care is essential to provide timely access to treatment for patients who present with a suspected acute stroke. In Northwestern Ontario (NWO), Canada, resources are limited and healthcare providers often must transfer stroke patients to different hospital locations to access to care within recommended timeframes. However, healthcare providers, who are often situated temporarily in NWO or provide care remotely, may lack sufficient information about which transfer route would be the most efficient and appropriate for the circumstance. Suboptimal decision-making may lead to multiple transfers before reaching definitive stroke care, resulting in poor outcomes and additional cost to the patient and the healthcare system. To address these issues, a comprehensive geomapping navigation and estimation application “NWO Navigate” was developed to improve timely access to definitive care. NWO Navigate uses a database to store and retrieve information about stroke services, historical emergency services data, Google Maps API, and OpenWeatherMap APIs to determine routing to definitive care facility.ResultsThe approach involved a retrospective simulation study for development of a geomapping system. Historical data from land and air emergency medical services of previous patient transportation times between hospital locations was collected and processed. This data was used to develop a prediction model using machine learning methods and incorporated into a mobile application. The aim of the application is to aid healthcare providers by presenting the best possible transfer options for a stroke patient based on the circumstances such as last time the patient was known to be well, patient location, treatment options, imaging availability.ConclusionThe obtained experimental results demonstrate that the proposed application has the potential to be a significant tool for healthcare providers navigating stroke care in NWO, impacting patient care and outcomes. The next step for the application is to undergo usability testing with the end-users to ensure the tool provides the assistance needed when caring for a stroke patient.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".