Evaluating the Therapeutic Effectiveness of Music Therapy in Post-Laparoscopic Ovarian Cystectomy Patients: A Single-Center Retrospective Study
Bibliographic record
Abstract
BACKGROUND: The therapeutic effects of music therapy on improving negative emotions and reducing pain are increasingly acknowledged. However, limited clinical research exists on its use in patients post-laparoscopic ovarian cystectomy (LOC). MATERIALS AND METHODS: A total of 147 patients undergoing LOC at our hospital were evaluated. Patients were divided based on the implementation time of music therapy: group A (72 patients, October 2020 to October 2021) received standard clinical treatment, while group B (75 patients, November 2021 to November 2022) received music therapy alongside routine care. The baseline data of patients and the scores of the Short-Form McGill Pain Questionnaire (SF-MPQ), Perceived Stress Scale (PSS), Beck Anxiety Inventory (BAI), and Patient Satisfaction Questionnaire were collected. Pain and psychological stress levels were compared on the first postoperative day and at discharge to assess the clinical value of each treatment approach. RESULTS: Group B exhibited significantly lower PSS, SF-MPQ, and BAI scores (P < 0.001 for all) and higher overall satisfaction at discharge (P < 0.001). These findings suggest that music therapy can reduce psychological stress, decrease pain levels, and improve mood in patients undergoing LOC. CONCLUSION: This study demonstrates that music therapy positively rehabilitates patients after LOC, offering new insights for future clinical treatment strategies.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".