Deconditioning of Long-Term Care Residents After Acute Hospitalization for a Hip Fracture in Ontario: A Retrospective, Population-Based Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To describe hospital-associated deconditioning experienced by long-term care (LTC) residents after hospitalization for a hip fracture in Ontario, Canada. DESIGN: Retrospective population-based cohort study using routinely collected data available through the Ontario Health Data Platform. SETTING AND PARTICIPANTS: LTC residents who were hospitalized for a hip fracture between June 1, 2018, and November 1, 2021. METHODS: Descriptive analyses were completed on resident age, sex, and comorbidities. We presented residents' length of hospital stay, admission to the intensive care unit, alternative level of care designation, and 30-day readmission following discharge. Deconditioning markers included both physical and psychological measures of resident functional status and cognition pre- and post-hip fracture hospitalization. RESULTS: We captured 4880 LTC residents who were hospitalized for a hip fracture between June 1, 2018, and November 1, 2021. Residents were on average 86.1 years old and predominately female (72%). Mean length of hospital stay was 6.97 days (SD 8.3), and 8% of residents died in hospital. Physical, cognitive, and psychological deconditioning were substantial postdischarge, especially in the ability to perform activities of daily living (10% dependent/totally dependent prehospitalization to 69% posthospitalization), balance while standing (20% severely impaired prehospitalization to 80% posthospitalization), health instability (6% with moderate to very high instability prehospitalization to 38% posthospitalization), and cognitive performance (1% with very severe impairment prehospitalization to 8% posthospitalization). CONCLUSIONS AND IMPLICATIONS: Markers of deconditioning demonstrate drastic declines in a variety of physical and psychological measures following hospitalization for a hip fracture among LTC residents.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".