Readmission Rates After Hip Fracture: Are There Prefracture Warning Signs for Patients Most at Risk of Readmission?
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to evaluate known and yet unknown risk factors associated with readmission to the hospital within 30 days after hip fracture. METHODS: In this study, we used the Cerner Health Facts Electronic Health Record database data from January to August 2015. The univariate association of each variable (discharge location, demographic details, and comorbidities) against the 30-day readmission status was evaluated using the Chi-square test or the Fisher exact test. The significant variables (P < 0.05) obtained by the univariate analysis were used to build the multivariate logistic regression model to evaluate the multivariate associations of the variables. RESULTS: Thirty-four thousand seven hundred ninety index admissions of 33,740 unique patients were included in the study cohort. The overall 30-day readmission rate for patients with hip fractures was 10.7%. We demonstrated a new variable not discussed in previous articles on this topic: patients with previous inpatient/emergency visits within the past year were more likely to be readmitted within 30 days after the hip fracture surgery (P < 0.001). CONCLUSION: For patients with hip fractures, particular efforts should be taken to optimize outcomes in those with recent hospitalizations and/or discharge to a location other than home.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".