2792 Hospital outcomes following hip fracture in older adults: does frailty matter?
Bibliographic record
Abstract
Abstract Objectives Older adults hospitalised with a hip fracture are at risk for adverse health outcomes depending on their level of frailty. This study examined how frailty levels prior to admission impacted length of stay (LOS), requirement for alternative level of care (ALC), returning home post-discharge, and mortality. Methods A random sample was generated from all hip fracture patients aged 65 and older admitted to a Level One Trauma Centre in New Brunswick, Canada from 2015–2019. This sample had their frailty level determined retrospectively using the Pictorial Fit-Frail Scale and the patients’ hospital electronic health record. Results Our study included 189 patients (mean age: 83.2 ± 8.2, 73.0% female), representing 91 not frail to mildly frail (48.2%; NF-MF), 32 moderately frail (16.9%; ModF), and 66 severely frail (34.9%; SF) patients. The ModF patients had a longer LOS (median: 20.0 days, IQR = 22.5) compared to NF-MF patients (median: 11.0 days, IQR = 10.0, p = 0.039, Kruskal-Wallis test) and SF patients (median: 8 days, IQR = 5.5, p < 0.0001, Kruskal-Wallis test). More ModF patients (56.3%) required an ALC stay in acute care compared to NF-MF (30.8%) and SF (28.8%) patients (p = 0.016, Chi-square test). More SF patients (28.8%) died in hospital or within six months post-discharge compared to NF-MF (8.8%) patients (p = 0.005, Chi-square test). Logistic regression revealed that both NF-MF (OR = 8.11, 95% CI: [3.12–21.06], p < 0.001) and ModF (OR = 5.18, 95% CI: [0.85–0.95], p = 0.007) patients had greater odds of returning home compared to SF patients when accounting for sex, age, and time to surgery. Conclusions A patient’s level of frailty prior to hospital admission impacts various health outcomes following a hip fracture and may provide helpful information for guiding treatment as well as discussions about health care.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".