The Impact of Geographic Deprivation Levels on Acute Ischemic Stroke Care in Alberta
Bibliographic record
Abstract
Introduction: Acute ischemic stroke is a neurological emergency that is associated with significant morbidity and mortality. Treatments for this condition aim to reperfuse the ischemic brain and are time sensitive in nature. To optimize patient outcome, strokes need to be recognized quickly, triaged appropriately, and started down the optimal treatment pathway. Unfortunately, previous works have suggested that there are inequalities in the provision of stroke care and outcomes based on patient socioeconomic status (SES). The objective of this thesis was to assess for disparities in the management and outcomes of patients who suffer ischemic stroke in Alberta, Canada based on a measure of neighbourhood SES. Methods: We performed three retrospective cohort studies using population level data from the Quality Improvement in Clinical Research Database. All patients were treated with IV Alteplase between January 1, 2017, and December 31, 2019. The outcomes of interest were treatment with endovascular thrombectomy (EVT), patient outcome (home-time), and treatment acuity (emergency room triage scores and stroke to needle time). Our independent variable of interest was an individual’s neighbourhood deprivation, as calculated by the Pampalon Index. We used regression modeling to assess for relationships between our outcomes and independent variable of interest. Results: Overall deprivation was significantly associated with the odds of being treated with EVT, yet this difference was no longer statistically significant after controlling for the distance an individual lives from the nearest comprehensive stroke centre. There was no significant association between deprivation level and home-time or stroke to needle time; however, being from the most deprived areas of Alberta was significantly associated with less acute emergency room triage scores being assigned. Conclusions: We identified potential areas of disparity in the treatment of acute ischemic stroke based on a measure of neighbourhood SES. However, these gaps did not lead to significantly worse patient outcomes in this study cohort. Future works should attempt to replicate these findings while including patients who were not treated with Alteplase.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".