Abstract 159: Impact Of Health Inequities On Outcomes Of Stroke In Children
Bibliographic record
Abstract
Introduction: Recent studies have reported access to initial imaging and underlying chronic disorders to be associated with post-stroke outcome in children. However, the influence of sociodemographic factors is yet to be investigated within the Canadian context. Our study explored the role of health inequities while considering the influence of clinical and radiological factors on post-stroke outcomes. Methods: A consecutive cohort of children >28 days-18 years of age diagnosed with arterial ischemic stroke between 2004 and 2019 at a comprehensive stroke centre in Ontario were included. Patient residential postal codes were linked to the Ontario Marginalization Index including area-level data on income, education, single-parent families, and housing quality. Post-stroke outcomes were assessed using the validated Pediatric Stroke Outcome Measure - Severity Classification System (PSOM-SCS). Poor outcome was defined as moderate-to-severe deficit at discharge or at 18 months from the onset of stroke. Univariable and multivariable logistic regression models were developed to examine the influence of material deprivation on neurological outcomes while controlling for demographic, clinical, and radiological factors. Results: Amongst 234 children, predictors of poor outcome at discharge included moderate-to-severe stroke at presentation (OR = 4.00, p < 0.05) while the presence of a single infarct may protect the patient from poor outcome at discharge (OR = 0.32, p < 0.05). Predictors of poor outcome at 18 months post-stroke included patients from moderately deprived neighborhoods (OR = 5.36, p < 0.05), stroke onset between 2014 and 2019 (OR = 7.44, p < 0.05), or presence of a left cerebral hemispheric infarction (OR = 8.20, p < 0.05). Conclusion: Our study demonstrated that stroke severity and the number of infarcts were important in determining outcome at discharge whereas neighbourhood-level material deprivation, year of onset, and infarct location predicted outcome at 18 months from the onset of stroke. Further research is needed to explore the role of broader social determinants of health in predicting stroke outcomes longitudinally over time.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".