Post-discharge Home Care Services Use, Long-Term Care Placement, and Survival in Older Adults with Major Trauma: a Population-Based Cohort Study from Ontario, Canada
Bibliographic record
Abstract
Background: There are few studies describing health services use and longer-term survival in older adults with major injuries. Our objectives were to characterize the outcomes of older adults experiencing a major injury, and to assess for associations between injury and rates of home-care services use (HCS), long-term care (LTC) placement, and survival. Methods: We conducted a retrospective case-control study including adults 65 years or older admitted for a major injury (trauma survivors) or were uninjured (controls) between 2009 and 2023 in Ontario, Canada. Trauma survivors were matched 1:1 to controls based on age, sex, and number of comorbidities. Primary outcomes were rates of HCS use and LTC admissions for up to five years following discharge. Secondary outcomes were types of HCS use and survival. Multivariate regression was used to compare rates of HCS and LTC use. Cox proportional hazards models were used to assess time to LTC admission or death. Results: The study cohort consisted of 31,508 individuals. Older adult trauma survivors experienced a twofold increased rate of home-care service use (odds ratio [OR]: 2.3, 95% confidence interval [CI]: 2.1-2.4), a 30% increased rate of LTC admissions (OR: 1.3, 95% CI: 1.0-1.6), and a 40% increased rate of death (hazard ratio: 1.4, 95% CI: 1.4-1.5). Conclusions: Compared to controls, older adults surviving trauma use HCS at significantly higher rates, require LTC placement more frequently, and experience a decreased rate of survival.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".