Demineral Drinking Water As A Bladder Irrigation During TURP (Transurethral Resection Of The Prostate) On Serum Electrolyte Levels
Bibliographic record
Abstract
Introduction: TURP procedure is recommended to treat symptoms of Benign Prostatic Hyperplasia (BPH) indicated for surgery. Irrigation method is the main factor in the success and evaluation of TURP syndrome. There is no ideal irrigation fluid or consensus result of the best irrigation fluid for TURP. Several irrigation materials have been developed related to effectiveness and cost considerations. Demineralized drinking water is an alternative for bladder irrigation of patients with TURP. The purpose of this study was to analyze the use of demineralized drinking water on serum electrolyte levels in patients before and after TURP. Methods: Using the pre-experiment method with a pre-post-test one group design approach, samples were selected using a purposive sampling technique of 15 respondents, analysis using the Wilcoxon signed ranks test. Results: The results of the univariate analysis showed that almost all respondents received an irrigation volume of 20 liters (93.3%), most respondents were irrigated for 60 minutes (73.3%). The sig.(2-tailed) value of serum Sodium 1.00 > 0.05, Potassium 0.655 > 0.05, and Chloride 0.655 > 0.05, showed no significant difference in the average results of serum electrolyte examination between pre and post-test. Conclusions: There is no effect of using demineralized drinking water as a bladder irrigation fluid in TURP procedures on serum electrolyte levels in BPH patients. The results of this study provide a strong basis for considering the use of demineralized drinking water as an option in clinical practice for bladder irrigation in TURP procedures.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".