Age‐Specific and Sex‐Specific Trends in Life‐Sustaining Care After Acute Stroke
Bibliographic record
Abstract
Background Temporal trends in life-sustaining care after acute stroke are not well characterized. We sought to determine contemporary trends by age and sex in the use of life-sustaining care after acute ischemic stroke and intracerebral hemorrhage in a large, population-based cohort. Methods and Results We used linked administrative data to identify all hospitalizations for acute ischemic stroke or intracerebral hemorrhage in the province of Ontario, Canada, from 2003 to 2017. We calculated yearly proportions of intensive care unit admission, mechanical ventilation, percutaneous feeding tube placement, craniotomy/craniectomy, and tracheostomy. We used logistic regression models to evaluate the association of age and sex with life-sustaining care and determined whether trends persisted after adjustment for baseline factors and estimated stroke severity. There were 137 358 people with acute ischemic stroke or intracerebral hemorrhage hospitalized during the study period. Between 2003 and 2017, there was an increase in the proportion receiving care in the intensive care unit (12.4% to 17.7%) and mechanical ventilation (4.4% to 6.6%). There was a small increase in craniotomy/craniectomy, a decrease in percutaneous feeding tube use, and no change in tracheostomy. Trends were generally consistent across stroke types and persisted after adjustment for comorbid conditions, stroke-center type, and estimated stroke severity. After adjustment, women and those aged ≥80 years had lower odds of all life-sustaining care, although the disparities in intensive care unit admission narrowed over time. Conclusions Use of life-sustaining care after acute stroke increased between 2003 and 2017. Women and those at older ages had lower odds of intensive care, although the differences narrowed over time. Further research is needed to determine the reasons for these findings.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".