Cost and determinants of acute kidney injury after elective primary total joint arthroplasty
Bibliographic record
Abstract
Background Acute kidney injury (AKI) is a serious complication after major surgery, which may lead to increased morbidity and mortality. The aim of this study was to identify cost and determinants of AKI after total joint arthroplasty. Methods A retrospective case-controlled study was conducted with 1719 primary elective total hip or knee replacements performed from January 2004 through September 2015 at an urban teaching hospital. Patients who developed AKI were matched in a 1:3 ratio with those in a control group who did not develop AKI based on age, sex, race, operated joint, and comorbidities including hypertension and diabetes. Increased postoperative serum creatinine was considered indicative of AKI. Results Fifty-four patients (3.1%) had AKI that was significantly associated with increased length of hospital stay (8.07 days) compared with that of the control group (4.50 days, P < .0001) and incurred significantly higher hospital charges ($224,533) than those of the control group ($142,753, P < .0001). We identified high body mass index, undergoing bilateral surgery in one session, high estimated blood loss, and longer duration of surgery as significant risk factors for AKI in univariate analysis. Elevated preoperative creatinine, large postoperative drop in hemoglobin, and high American Society of Anesthesiologists physical status scores were significant independent predictors of AKI in multivariate analysis. Conclusions Health-care providers and patients should work together to manage risk factors and to lower the risk of morbidity and mortality, longer in-hospital stay, and high associated costs of AKI.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".