Health outcomes of older adults in long-term care homes following hospitalization for hip fracture surgery
Bibliographic record
Abstract
Abstract Hip fractures are among the most common and debilitating conditions in the older adult population, particularly for those living in long-term care (LTC) homes. These fractures can reduce quality of life and increase mortality risk. However, surgical management carries potential complications. As a result, older adults and their families often confront the difficult decision of whether to undergo surgery. In this study, we compare outcomes of hospitalized older adults (65+ years old) in Ontario LTC homes that did or did not undergo hip fracture surgery. Data was obtained through the Institute for Clinical Evaluative Sciences (ICES) from 2015 to 2019. Older adults were categorized based on presence of a recorded surgical code. Following this, baseline characteristics and post-discharge outcomes, including mortality, were retrieved from ICES. Outcomes were further stratified by various frailty indices. Among 5,279 older adults meeting inclusion criteria (mean age: 87.01 years, 74.2% female), 4,547 (86.1%) underwent surgery. At baseline, non-surgically managed older adults had higher rates of osteoporosis, hypertension, renal failure, as well as greater functional impairment. Six months post-discharge, older adults who were surgically managed reported fewer deaths in the hospital (4.7% vs 12.8%, p < 0.0001), were less likely to experience frequent pain (6.6 vs 7.3%, p < 0.0001), and saw improved functional independence (13.8% vs 11.9%, p < 0.0001). However, they were also more likely to be physically restrained (11.5% vs 6.3%, p < 0.0001). These findings highlight key factors that influence the decision to undergo hip fracture surgery, which offers invaluable insights for clinical decision-making and care planning in LTC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".