Harnessing Routinely Collected Health Data for Global Monitoring of Stroke: Roadmap and Vision for INSPIRE-STROKE
Bibliographic record
Abstract
INTRODUCTION: Sustainable and low-cost data systems for national and global surveillance of stroke are urgently needed to address the growing burden of stroke. Routinely collected health data (including registries and administrative data) are proliferating at a rapid pace, offering promise for systematic and enduring global stroke surveillance. However, several challenges exist in utilising these routinely collected data from across the globe for global stroke surveillance, such as non-standardised definitions and coding, missingness of data, and lack of transparent or reproducible methods. We aim to describe the vision and methods for a new global collaboration to leverage and harmonise population-level health data for global stroke surveillance. METHODS: The International Network for Standardised Population Insights and Real-world Evidence for STROKE (INSPIRE-STROKE) was established in October 2023 and currently includes 39 collaborators from 16 countries. The vision of INSPIRE-STROKE is to develop new methods that will harmonise and combine health databases across the world to facilitate reliable and robust multi-country stroke surveillance. Through this scientific community, we are initially collaborating to: (1) summarise existing methods for calculating and reporting measures of post-stroke outcomes using routinely collected health data; (2) develop consensus-based standards for analysing routinely collected health data on post-stroke outcomes; and (3) conduct proof-of-concept studies to align variables/definitions in routinely collected health data and create standardised statistical code to measure post-stroke outcomes (e.g., medication adherence, readmissions, and mortality) according to consensus-based definitions. CONCLUSION: INSPIRE-STROKE will support more reliable investigations into country-level trends in stroke incidence and outcomes, by leveraging routinely collected health data at a global scale. The large and diverse data compiled for INSPIRE-STROKE could facilitate exploration of rare stroke outcomes, particularly among under-represented groups (e.g., pregnant women, children). INSPIRE-STROKE will strengthen health policy and resource planning by providing high-quality evidence to improve access to stroke care and maximise patient outcomes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".