Health Care Resource Use After Acute Stroke in the Glycine Antagonist in Neuroprotection (GAIN) Americas Trial
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: To compare 3-month stroke outcomes and stroke-related health care resource use between the US and Canada in the Glycine Antagonist in Neuroprotection (GAIN) Americas study. Delivery of medical care for stroke patients, often driven by efforts to curb costs, varies substantially between countries. Data on the potential impact of these variations on clinical outcomes are sparse. METHODS: The analysis of health care resource included total length of stay (LOS) in hospital, intensive care unit (ICU), and acute-care ward or rehabilitation unit, or both; number of outpatient rehabilitation sessions and visits to a physician; place of residence after discharge; and employment status. Cox proportional hazards models and logistic regression were used to calculate survival hazards and predictors of favorable functional outcome (Barthel Index of 95 to 100). RESULTS: One thousand six hundred four patients who were independent before stroke (mean age: 69.9+/-12.7 years, 53% men, 85% ischemic stroke, 69% in the US) were included. Three-month survival and functional outcome did not differ between the US and Canada. Survival rate was 80% in both countries. Favorable functional outcome was achieved in 43% of Canadian and 47% of US patients. Fewer Canadian patients received treatment in ICU (19% versus 63% in the US), and Canadians had longer stays in hospital or rehabilitation facility (median: 33 days versus 16 days in the US). CONCLUSIONS: Despite similar 3-month survival and functional outcome, patterns of health care resource varied substantially between the US and Canada. US patients had more intensive early care; Canadian patients had longer hospitalizations and rehabilitation care. Further research is required to determine the most cost-effective treatment and rehabilitation plan for people who have a stroke.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".