Clinical Utiliy of Routine Postoperative Laboratory Tests After Laparoscopic Prostate Surgery
Bibliographic record
Abstract
Background: Post-operative protocols, including blood tests, are frequently implemented to standardize care and as guarantees of safety before discharge. They might however be unnecessary after minimally invasive surgery. Our objective was to determine the clinical utility of routine postoperative blood test after laparoscopic prostate surgery. Methods: A retrospective review of 231 patients who underwent laparoscopic prostatectomy was conducted. The primary outcome was the rate of clinically significant blood loss, defined as a drop in hemoglobin level of 4 g/dL or the need for a blood transfusion. A logistic regression model was developed for the outcomes of interest. Results: Final review included 231 patients. Forty-five patients (19.5%) had at least one abnormal blood test parameter on the first post-operative day. Eleven patients (4.8%) had clinically significant blood loss, with four patients (1.7%) overall requiring a blood transfusion. All patients requiring a transfusion had a significant complication that was clinically evident; all other abnormal blood tests were mild and did not change routine care. Signs of hemodynamic instability were the main predictors of clinically significant blood loss on multivariable regression analysis, with an odds ratio (OR) of 4.14 (95% confidence interval (CI): 1.12 - 15.35; P = 0.034). Conclusions: Routine post-operative blood tests have low yield, seldomly changing care. Signs of hemodynamic instability were the main predictors of significant blood loss and can be used as triggers for laboratory testing. Reducing routine laboratory tests improves patients' experience, diminishes cost and hospital stay. Our results provide evidence to perform radical prostatectomies in a 1-day surgery setting. World J Nephrol Urol. 2024;13(1):13-18 doi: https://doi.org/10.14740/wjnu444
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".