Hospital Readmissions After Hospital Discharge for Hip Fracture: Surgical and Nonsurgical Causes and Effect on Outcomes
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVES: To examine the causes of hospital readmission after hip fracture and the relationships between hospital readmission and 6-month physical function and mortality. DESIGN: Prospective, multisite, observational cohort study. SETTING: Four hospitals in the New York City metropolitan area. PARTICIPANTS: Five hundred sixty-two patients hospitalized for hip fracture aged 50 and older and discharged alive in 1997-1998. MEASUREMENTS: Patient demographic characteristics, type of fracture and repair, comorbid conditions, postoperative complications, do not resuscitate status, and active clinical problems at the time of hospital discharge. Prefracture and 6-month mobility were measured using the Functional Independence Measure. Hospital readmissions and International Classification of Diseases, Ninth Revision principal diagnoses were ascertained from hospital admission/discharge databases, the New York Statewide Planning and Research Cooperative System, medical record review, and patient self-report. RESULTS: Eighty-two percent of participants were women, and 93% were white. Within 6 months after hospital discharge, 178 (32%) patients were readmitted to the hospital, with 45 (8%) readmitted more than once. Forty-seven of 233 readmissions (20%) occurred within the first 2 weeks after discharge, and 80 (34%) occurred within 4 weeks. Over 6 months, 89% of readmissions were for nonsurgical problems, of which infectious (21%) and cardiac (12%) diseases were the most common. In multivariate analyses, patients who were readmitted were more likely to require total assistance with ambulation at 6 months (odds ratio (OR) = 2.7, 95% confidence interval (CI) = 1.6-4.6) and to die (OR = 4.0, 95% CI = 2.2-7.3) than those not readmitted. CONCLUSION: Hospital readmissions after hip fracture are largely due to nonsurgical illness and are associated with increased morbidity and mortality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it