Abstract 185: Residual Stroke Morbidity and Post-acute Care Cost: the IMS III Economic Data Cohort
Bibliographic record
Abstract
Introduction: The IMS III trial included 1-year follow-up with prospectively collected data on resource use after stroke. While the trial showed no difference in 90-day clinical outcomes by treatment group, this cohort provides invaluable information on cost variations associated with post-stroke morbidity. We report the effect of residual stroke morbidity on cost of stroke care after discharge at 12 months post stroke. Methods: Among 470 subjects with moderate to severe stroke for whom economic data were collected (316 randomized to IV t-PA and endovascular therapy, 154 to IV t-PA alone), we estimated cumulative cost post discharge using cost weights derived from a 5% sample of US Medicare patients in 2012 with an admission for acute ischemic stroke with IV t-PA treatment. Cost weights included post-stroke rehabilitation hospital days, emergency care visits, hospital readmissions, medical office visits, rehabilitation therapy visits and nursing home days. Costs were summed at the level of the subject and estimated for the subset defined by NIH Stroke Scale Score (NIHSS) at day 5, and Modified Rankin Score (mRS) and Barthel Index (BI) at 3 months post stroke. Subjects who died during the initial hospital admission or who had no score at day 5 or at 3 months were not included in our analysis. Age-adjusted, log-transformed costs were compared. Results: There was a 6 fold difference in the cost of follow-up care by lowest and highest NIHSS at day 5 (p<.0001). Similarly large differences by outcome category were observed for both the mRS (p<.0001) groups and subjects defined by the BI (p<.0001) at 3 months (see Figure). Conclusion: Residual stroke morbidity has a large effect on the long-term cost of stroke care, with an effect size of over 600%. Interventions that improve the residual morbidity after stroke as early as day 5 may be expected to result in substantial post discharge cost savings.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".