Effectiveness of Low-Frequency Electrical Stimulation for Radical Hysterectomy Women: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
INTRODUCTION: The aim of the study was to explore the effects of low-frequency electrical stimulation (LFES) in preventing urinary retention after radical hysterectomy (RH) in women with cervical cancer. METHODS: Seven electronic bibliographic databases were searched from inception to December 25, 2021. The mean difference (MD) or risk ratio (RR) with its corresponding 95% CI was selected as effect size. The meta-analysis of all data was conducted using RevMan 5.4 and the evidence was summarized according to GRADE (the grading of recommendation, assessment, development, and evaluation). RESULTS: Twelve randomized control trials consisting of 1,033 women with cervical cancer who had undergone RH were included. Compared with women in the control group, women receiving LFES had improved therapeutic effect (RR = 0.22, 95% CI: 0.16-0.29) and reduced residual urine volume (MD = -32.27, 95% CI: -34.10 to -30.43) and catheter retention time (MD = -4.46, 95% CI: -5.17 to -3.76) following treatment. Muscle strength scores of pelvic floor type I and type II muscle fibers in the LFES group were also higher than in the control group (MD = 1.07, 95% CI: 0.91-1.24). CONCLUSION: LFES may be an effective auxiliary treatment for women with cervical cancer after hysterectomy, which can help reduce the duration of indwelling urethral catheter and residual urine volume.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.023 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".