Imperfect Patients: Disparities in Treatment of Stroke Patients with Premorbid Disability
Bibliographic record
Abstract
BACKGROUND: Despite the high proportion of stroke patients with a pre-existing impairment, patients with disabilities are often excluded from stroke treatment trials. Trials are designed for "perfect patients": patients who are functionally independent and thus generally younger with fewer comorbidities; ironically, such patients are less likely to experience stroke than those with premorbid disability. Exclusionary practices in trials may translate into disparities in stroke care in practice. Through a review of literature, our purpose is to illuminate how people with disabilities are treated across the care continuum following a stroke. METHODS: We completed a qualitative systematized review of articles pertaining to the care of patients with premorbid disability and stroke and their outcomes. Using a critical disability studies' theoretical lens, we analyzed inequity across the stroke care continuum. FINDINGS: Among 24 included studies, we found evidence that people with disabilities did not receive equitable access to treatment ranging from being admitted to stroke units to receiving post-stroke rehabilitation. However, observational studies suggest that stroke therapies may be beneficial in selected patients with disabilities when measures of success are framed more achievable (e.g. return to pre-stroke status). This leaves us concerned about how people with pre-existing impairments might be structurally disabled within current systems of stroke care. CONCLUSION: We use our critical disability studies' theoretical lens to argue that an intersectional approach to stroke treatment is much needed if we are to remedy structural inequities embedded throughout the care continuum.
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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 | 0.024 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".