Electrocoagulation Accounts for a Significant Portion of Discrepancy Between Preoperative Ultrasonography Prostate Size Estimation and Resected Tissue Weight
Bibliographic record
Abstract
Background and Objectives: Transurethral resection of the prostate (TURP) is considered to be the gold standard surgical procedure for medium-sized prostates. However, there is a drastic difference between the weight of resected tissue reported by the pathologist and the sonographic estimate of prostate weight before the operation. The present study investigates the role of electrocoagulation in tissue loss during monopolar (M-) and bipolar (B-) TURP. Methods: In this experimental in vitro study, 33 prostate glands removed from patients by open suprapubic prostatectomy were roughly divided in half and the weight of each half was recorded precisely. One half of the specimen was resected using a monopolar resectoscope and the other half was resected by bipolar resectoscope in an out-of-body simulated condition. The weight of obtained chips was measured and the amount of lost tissue was calculated. Data from prostates with final pathologic diagnosis of benign prostatic hyperplasia were included in the analysis. The mean difference of prostate weight before and after out-of-body resection and also the proportion of tissue loss between M-TURP and B-TURP were compared using t-test. Results: Data from 62 prostate halves were included in the final analysis. The mean weight of samples after M-TURP was considerably lower than before resection (38.0 ± 6.9 and 48.7 ± 9.0, respectively, p < 0.001). Similarly, the mean weight of prostate samples after B-TURP was lower than the weight before resection (36.2 ± 7.3 and 48.4 ± 9.1, respectively, p < 0.001). However, the ratio of tissue loss in B-TURP (25.3% ± 2.5%) was significantly higher than in M-TURP (21.3% ± 3.7%) (p < 0.001). Conclusion: Prostate resection using electrocoagulation leads to a significant prostate tissue loss due to heat effect. Furthermore, tissue loss in B-TURP is slightly more than M-TURP.
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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) | 0.000 | 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".