Impact of Disability Status on Ischemic Stroke Costs in Canada in the First Year
Bibliographic record
Abstract
BACKGROUND: Longitudinal, patient-level data on resource use and costs after an ischemic stroke are lacking in Canada. The objectives of this analysis were to calculate costs for the first year post-stroke and determine the impact of disability on costs. METHODOLOGY: The Economic Burden of Ischemic Stroke (BURST) Study was a one-year prospective study with a cohort of ischemic stroke patients recruited at 12 Canadian stroke centres. Clinical history, disability, health preference and resource utilization information was collected at discharge, three months, six months and one year. Resources included direct medical costs (2009 CAN$) such as emergency services, hospitalizations, rehabilitation, physician services, diagnostics, medications, allied health professional services, homecare, medical/assistive devices, changes to residence and paid caregivers, as well as indirect costs. Results were stratified by disability measured at discharge using the modified Rankin Score (mRS): non-disabling stroke (mRS 0-2) and disabling stroke (mRS 3-5). RESULTS: We enrolled 232 ischemic stroke patients (age 69.4 ± 15.4 years; 51.3% male) and 113 (48.7%) were disabled at hospital discharge. The average annual cost was $74,353; $107,883 for disabling strokes and $48,339 for non-disabling strokes. CONCLUSIONS: An average annual cost for ischemic stroke was calculated in which a disabling stroke was associated with a two-fold increase in costs compared to NDS. Costs during the hospitalization to three months phase were the highest contributor to the annual cost. A "back of the envelope" calculation using 38,000 stroke admissions and the average annual cost yields $2.8 billion as the burden of ischemic stroke.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".