Novel Network Analysis of County- and Individual-Level Factors Associated With Functional Outcomes After Stroke
Bibliographic record
Abstract
BACKGROUND: Social determinants are known to impact stroke risk and poststroke outcomes. Using complexity science, we examined interrelations between county- and individual-level social and clinical determinants influencing stroke functional outcomes. METHODS: We examined a retrospective cohort of 2 961 664 patients diagnosed with acute ischemic or hemorrhagic stroke from 2218 US hospitals participating in the Get With The Guidelines-Stroke Registry from 2013 to 2019, linked by ZIP code with the county-level institute for health metrics and evaluation data. We constructed multilayer networks, estimating mixed graphical models of 32 nodes representing social and clinical factors. Networks included 4 layers of factors: (1) county-level social, (2) individual-level social, (3) clinical comorbidities, and (4) hospital encounters. Networks were estimated for patients with less favorable (modified Rankin Scale score 3–6) versus favorable (modified Rankin Scale score 0–2) outcomes. We compared network structure and node centrality measures between groups using bootstrap permutation analyses, identifying influential (hub) nodes. RESULTS: The overall influence of social determinants (global connectivity) was greater in patients with less favorable outcomes ( P <0.001). Homelessness and Black race were hub nodes, indicating their role in mediating relationships between social and downstream clinical factors in patients with less favorable outcomes. Being uninsured had greater influence (closeness centrality; P <0.001) in patients with less favorable outcomes, indicating its role in amplifying the effects of social determinants. Greater county-level high school completion ( P <0.001) and a lower proportion of the population living below the US poverty line ( P =0.030) were directly associated with faster onset-to-arrival time in patients with less favorable functional outcomes. The clinical-social determinant network explained 34% of the variance of modified Rankin Scale scores. CONCLUSIONS: Social determinants have a substantial influence on functional outcomes after stroke. County-level poverty directly affected onset-to-arrival time and quality of care. Health insurance status and homelessness were influential and modifiable patient-level factors that may serve as critical leverage points for future interventions aimed at improving outcomes.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".