Healthcare Resource Utilization in the Three Months after Acute Stroke: A Report on 1604 Patients from the Glycine Antagonist in Neuroprotection (GAIN) Americas Trial
Bibliographic record
Abstract
P226 Background/objective: Data on healthcare resource utilization (RU) in acute stroke will assist in evaluation of cost-efficacy and cost-effectiveness of new treatments. We present results of a substudy of resource utilization within GAIN Americas, a multi-center, randomized, placebo-controlled, phase 3 clinical trial of the glycine antagonist GV150526 in acute stroke, conducted in the US and Canada. Previously reported clinical results showed no difference in clinical outcome by treatment at 3 months. Methods: RU data included pre-stroke and Month 3 residence and employment status, hospital length of stay (LOS) by ward type, discharge destination, and outpatient rehabilitation services. There were no treatment differences on RU endpoints, so data were combined across treatment groups. Results: The study population comprised 1604 acute stroke patients (85% ischemic; median age: 72; female: 48%; median NIHSS score: 12). Case fatality was 13% in hospital, 21% by Month 3. Median hospital LOS was 9 days (mean=15), including 3 days (mean = 4.5) in intensive care and 4.5 days (mean=6.8) in step-down. Discharge destinations included rehabilitation facility (49%), home (27%) and nursing home (11%). Pre-stroke, 87% lived in own home, but at 3 months only 62% lived at home. Outpatient rehabilitation services included occupational therapy (31%), physical therapy (39%), speech therapy (20%) and psychotherapy (2%). Pre-stroke, 21% were employed full- or part-time; at Month 3, 6% were employed. Of those employed pre-stroke, 60% reported being unable to work for health reasons at Month 3. Conclusion: These data describe the social and economic burden of stroke in a well-defined cohort. Such information should be valuable to care providers at many levels of the healthcare system and to health outcomes researchers. Prospective collection of detailed RU data from a large clinical trial is feasible and worthwhile.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".