Pelvic Floor Muscle Exercise by Biofeedback and Electrical Stimulation to Reinforce the Pelvic Floor Muscle after Normal Delivery
Bibliographic record
Abstract
PURPOSE: This study was conducted to investigate the effectiveness of pelvic floor muscle exercise using biofeedback and electrical stimulation after normal delivery. METHODS: The subjects of this study were 49 (experimental group: 25, control group: 24) postpartum women who passed 6 weeks after normal delivery without complication of pregnancy, delivery and postpartum. The experimental group was applied to the pelvic muscle enforcement program by biofeedback and electrical stimulation for 30 minutes per session, twice a week for 6 weeks, after then self-exercise of pelvic floor muscle was done 50-60 repetition per session, 3 times a day for 6 weeks. Maximum pressure of pelvic floor muscle contraction (MPPFMC), average pressure of pelvic floor muscle contraction (APPFMC), duration time of pelvic floor muscle contraction (DTPFMC) and the subjective lower urinary symptoms were measured by digital perineometer and Bristol Female Urinary Symptom Questionnaire and compared between two groups prior to trial, at the end of treatment and 6 weeks after treatment. RESULTS: The results of this study indicated that MPPFMC, APPFMC, DTPFMC were significantly increased and subjective lower urinary symptoms were significantly decreased after treatment in the experimental group than in the control group. CONCLUSIONS: This study suggested that the pelvic floor muscle exercise using biofeedback and electrical stimulation might be a safer and more effective program for reinforcing pelvic floor muscle after normal delivery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.002 | 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".