Effectiveness of Biofeedback Therapy in Treating Bladder Function Disorders
Bibliographic record
Abstract
Bladder dysfunction is a frequent health problem faced by many individuals around the world. Biofeedback therapy has become an increasingly commonly used approach to address these disorders, focusing on improving pelvic muscle control and body awareness. However, few studies have specifically evaluated the effectiveness of biofeedback therapy in addressing impaired bladder function, especially in specific populations. This study aimed to evaluate the effectiveness of biofeedback therapy in addressing bladder dysfunction in adults with incontinence or urinary retention. This study used a randomized controlled clinical research design. They were randomly divided into two groups, an intervention group that received biofeedback therapy for 8 weeks, and a control group that received no additional intervention. Measurements were made using standardized clinical evaluation scales and objective measurements of urine volume. The results of this study showed that the intervention group receiving biofeedback therapy had significant improvements in pelvic muscle control, incontinence frequency, and volume of retained urine resolved compared to the control group. This improvement was also maintained in the follow-up period after 3 months of intervention. The conclusion of this study is that biofeedback therapy is proven to be effective in overcoming bladder function disorders in adult individuals with complaints of urinary incontinence or retention. This approach can be an effective and sustainable treatment option to improve the quality of life and well-being of individuals affected by bladder dysfunction. Further research is needed to explore more deeply the mechanisms and factors that influence the effectiveness of biofeedback therapy in different cases of bladder function disorders.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".