Cost of Spontaneous Intracerebral Hemorrhage in Canada During 1 Decade
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Spontaneous intracerebral hemorrhage (ICH) is widely considered to be the most devastating form of stroke in North America. Currently there is no clear understanding of the cost of treatment in Canada and thus no way of understanding how to manage ICH spending in this country. METHODS: We used a cohort study design to report and to examine the cost of ICH hospital care in a Canadian health center during 1 decade. Economic, treatment, and patients data were obtained from clinical and administrative sources. RESULTS: Analyses were performed using 987 consecutive patients with ICH from 1999 to 2008. The total inflation-adjusted cost of care was highly variable (median cost per discharge, $10,544.45 and $363.54 [min] to $265 470.43 [max] United States Dollars). Total cost did not change significantly during the decade. Patients age (cost change per year older, -$114.06 and -$189.01 to -$38.78) and in-hospital mortality (cost change for death, -$5092.84 and -$6270.65 to -$3697.09) were significantly associated with lower cost, whereas Charlson Comorbidity Index (cost change for ≥1, $5726.27 and $3965.36 to $7755.45), having surgery (cost change for surgery, $25,499.78 and $20,813.95 to $30,933.06), and admission National Institutes of Health Stroke Scale (cost change for ≥15 points, $7800.20 and $1637.78 to $17,026.38) were significantly associated with higher cost. CONCLUSIONS: To our knowledge, this is the most thorough published study to date to report and to examine predictors of ICH treatment costs in Canada. This study provides evidence that it may be reasonable to consider patients age, probability of death, level of comorbidity, need for surgery, and baseline ICH severity when forecasting health spending.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.001 | 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".