Nursing care of <scp>TURP</scp> and hyperglycemia integrating symptoms management model
Bibliographic record
Abstract
Abstract Transurethral resection of the prostate (TURP) is a surgical procedure often used to treat benign prostatic hyperplasia. TURP often results in multiple symptoms that worsen a patient's condition, such as hyperglycemia. The relationship between TURP and hyperglycemia is not direct, but it is important to consider the potential impact of hyperglycemia on individuals undergoing TURP. The most critical point in the TURP syndrome is early diagnosis and treatment. Nurses should be aware of the symptoms to prevent further outcomes. To optimise the nursing care, integrating of symptoms management model in TURP care is important as it provides a conceptual foundation for understanding patient care, guides clinical decision‐making, contributes to evidence‐based practice and fosters professional development. Also nurses can deliver high‐quality TURP and hyperglycemia care that meets the diverse needs of patients and contributes to positive health outcomes. However, a study describing the symptoms management of patients living with TURP syndrome and hyperglycemia is limited. Therefore, the article aims to explain the management of hyperglycemia among patients after TURP. The findings of this review are expected to help the nurses notice the symptoms and make accurate interventions along with evaluations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".