Effect of Kidney Disease on Hemiarthroplasty Outcomes After Femoral Neck Fractures
Bibliographic record
Abstract
OBJECTIVE: To compare the outcomes of patients with predialysis chronic kidney disease (CKD) or end-stage renal disease (ESRD) with the outcomes of patients with no kidney disease after hemiarthroplasty (HA) for femoral neck fractures (FNF). DESIGN: Retrospective review utilizing the Nationwide Readmissions Database. SETTING: National database incorporating inpatient data from 22 states. PATIENTS: Using the Nationwide Readmissions Database, 214,399 patients who underwent HA after FNF between 2010 and 2014 were identified and divided into 3 groups using ICD-9 diagnosis codes: no kidney disease (n = 176,300, 82%), predialysis CKD (n = 34,400, 16%), and ESRD (n = 3,698, 2%). INTERVENTION: HA for FNF. MAIN OUTCOME MEASUREMENT: Mortality, blood transfusion, and postoperative complications during index hospitalization. Hospital readmission, postoperative dislocation, periprosthetic fracture, and revision surgery within 90 days of surgery. RESULTS: Compared to patients with no kidney disease, ESRD patients had an increased risk of mortality [odds ratio (OR) = 3.76, 95% confidence interval (CI), 2.95-4.78], blood transfusion (OR = 2.35, 95% CI, 2.08-2.64), and postoperative complications (OR = 1.64, 95% CI, 1.45-1.86) during the index hospitalization as well as an increased risk of 90-day hospital readmission (OR = 3.09, 95% CI, 2.72-3.50). Interestingly, even patients with predialysis CKD had an increased risk of mortality (OR = 1.80, 95% CI, 1.59-2.05), blood transfusion (OR = 1.66, 95% CI, 1.59-1.75), and postoperative complications (OR = 2.37, 95% CI, 2.25-2.50) during the index hospitalization as well as an increased risk of 90-day hospital readmission (OR = 1.43, 95% CI, 1.37-1.51). CONCLUSIONS: This retrospective cohort study demonstrates that both ESRD and CKD patients have worse outcomes compared to patients with no kidney disease after HA for FNF. LEVEL OF EVIDENCE: Prognostic Level III. See instructions for authors for a complete description of levels of evidence.
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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 | 0.000 | 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) | 0.002 | 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 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".