Catheterizations Reduced Following a Protocol Change for Postoperative Urinary Retention Following Total Joint Arthroplasty With Spinal Anesthesia
Bibliographic record
Abstract
INTRODUCTION: Postoperative urinary retention (POUR) is a common concern after total joint arthroplasty (TJA). However, overdiagnosis of POUR by bladder scans may lead to unnecessary interventions and associated complications. The purpose of this study was to determine the viability of a selective bladder scanning protocol to reduce overdiagnosis of POUR following TJA. METHODS: A study of 500 consecutive patients operated on by 13 surgeons underwent a novel bladder scanning protocol designed to reduce overdiagnosis of POUR (selective protocol) through selective bladder scanning of only patients symptomatic for urinary retention. The protocol was then compared with the previous bladder scanning protocol under which 500 patients received routine bladder scans following TJA regardless of symptoms. The primary outcome of interest was the catheterization rate. Chi-squared and Student t-tests were used for analyses. RESULTS: Catheterization rates were markedly reduced when following the novel protocol (46.6% standard versus 38.2% selective; P = 0.009). Prevoid bladder scan volumes were markedly higher in the selective protocol cohort (459 ± 278 mL, 746 ± 226 mL; P ≤ 0.001), whereas postvoid bladder scan volumes were the same (247 ± 250 mL, 243 ± 271 mL; P = 0.859). Preoperative evaluation of urinary problems, intraoperative urinary complications, postoperative urinary symptoms, and spinal medication type were similar in each cohort. Length of service, rate of same-day discharge, total surgical time, and readmission rate were also similar. CONCLUSION: The selective protocol led to a notable reduction in catheterizations when compared with the standard protocol. This was done safely, without increased rates of urinary-related complications during or following surgery. These findings suggest that the selective protocol should be considered as a viable alternative to reduce overdiagnosis of POUR in TJA patients.
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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 | 0.003 | 0.010 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".