Hospital Readmissions After Hospital Discharge for Hip Fracture: Surgical and Nonsurgical Causes and Effect on Outcomes
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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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".