Barriers to Discharge of Hip Fracture Patients From An Academic Hospital: A Retrospective Data Analysis
Bibliographic record
Abstract
Introduction: Adherence to best practices for care of hip fracture patients is fundamental to decreasing morbidity and mortality in older adults. This includes timely transfer from the hospital to rehabilitation soon after their surgical care. Hospitals experience challenges in implementing several best practices. We examined the potential barriers associated with timely discharge for patients who underwent a hip fracture surgery in an academic hospital in Ontario, Canada. Methods: We conducted a retrospective cross-sectional review of a local database. We used descriptive statistics to characterize individuals according to the time of discharge after surgery. Multivariable binary logistic regression was used to evaluate factors associated with delayed discharge (>6 days post-surgery). Results: A total of 492 patients who underwent hip fracture surgery between September 2019 and August 2020 were included in the study. The odds of having a delayed discharge occurred when patients had a higher frailty score (odds ratios [OR] 1.19, 95% confidence interval [CI] 1.02;1.38), experienced an episode of delirium (OR 2.54, 95% CI 1.35;4.79), or were non-weightbearing (OR 3.00, 95% CI 1.07;8.43). Patients were less likely to have a delayed discharge when the surgery was on a weekend (OR .50, 95% CI .32;.79) compared to a weekday, patients had a total hip replacement (OR .28, 95% CI .10;.80) or dynamic hip screw fixation (OR .49, 95% CI .25;.98) compared to intramedullary nails, or patients who were discharged to long-term care (OR .05, 95% CI .02;.13), home (OR .26, 95% CI .15;.46), or transferred to another specialty in the hospital (OR .49, 95% CI .29;.84) compared to inpatient rehabilitation. Conclusions: Clinical and organizational factors can operate as potential barriers to timely discharge after hip fracture surgery. Further research is needed to understand how to overcome these barriers and implement strategies to improve best practice for post-surgery hip fracture 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".