Parameters affecting urologic complications after major joint replacement surgery.
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVES: Peri-operative bladder management after major arthroplasty procedures remains controversial. The purpose of this study was to assess the risk of urological complications in those patients undergoing hip or knee joint replacement. As well, we identified those factors that may affect the likelihood of developing complications. METHODS: Two hundred and twenty-one consecutive patients receiving a total knee or hip arthroplasty were reviewed. The outcomes measured were prolonged urinary retention, as well as urinary tract infections and the development of a septic prosthesis. Statistical significance of any predisposing factors identified was determined using a two-tailed Fisher exact test. RESULTS: Urological complications in the cohort were common at 47%, with patients having hip arthroplasty being at higher risk (p < 0.03). Despite this there was a low incidence of documented infections. Increased rates of urinary retention were identified in those who received intrathecal narcotics (p < 0.02), as well as those who suffered from hypertension (p < 0.05). Gender and anesthetic techniques (general or regional) did not affect the rate of complications. There was a decrease in urological complications when bladder management included peri-operative catheterization rather than expectant management. CONCLUSIONS: Bladder management is a significant problem for patients after hip and knee arthroplasty as urinary retention was identified in almost half of the patients. Parameters that may identify those with higher risks include patients with hypertension and those who receive intrathecal narcotics. In high-risk patients, the practice of utilizing a catheter peri-operatively may decrease the risk of multiple post-operative catheterizations without increasing the rate of infections.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".