Abstract WMP99: A Novel Network Analysis of County- and Individual-Level Factors Associated With Functional Outcomes After Stroke
Bibliographic record
Abstract
Background: Social circumstances may contribute to health disparities in patients with stroke through indirect and interdependent mechanisms that are not well understood. Objective: This study used complexity science methodology to examine the interplay between county- and individual-level social and clinical factors influencing stroke functional outcomes. Methods: As part of the Get With the Guidelines-Stroke (GWTG-S) Data Challenge, the GWTG-S Registry was merged with county-level Institute for Health Metrics and Evaluation data. Patients diagnosed with stroke (ischemic, subarachnoid, or intracerebral hemorrhage) were included. Multilayer networks were constructed by estimating mixed graphical models of 32 nodes across four layers, including social (county- and patient-level) and clinical (comorbidities and encounter) factors. Social determinant networks were estimated for patients with less favorable (ie., functional dependence or death, modified Rankin Score [mRS] 3-6) versus favorable outcomes (ie., functional independence, mRS 0-2). Network structure and node centrality were compared between mRS groups using bootstrap permutation analyses. Hub nodes were defined by betweenness centrality and facilitate effects across the network. Results: From 2013-2019, 990 721 (62.3%) stroke patients had mRS of 3-6 and 597 477 (37.6%) had mRS 0-2 at discharge. Compared to the mRS 0-2 group, the mRS 3-6 group’s social determinants network had greater global connectivity (p<0.001), and homelessness (p<0.001) and Black race (p<0.001) were hub nodes. Unique to the mRS 3-6 group, younger patients were more likely to identify as homeless (p=0.031), uninsured (p=0.001), and live in a county with lower per capita income (p<0.001). Conclusions: The effects of social determinants were significantly greater in patients with less favorable functional outcomes. Homelessness and race play critical roles in mediating the impacts of county- and individual-level social determinants on downstream disparities in stroke outcomes. Particularly among younger patients, housing, insurance status, and income may serve as a critical leverage point for county-level interventions to improve function after stroke.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".