Effetti dell'ascolto della musica in pazienti ortopedici: uno studio randomizzato controllato
Bibliographic record
Abstract
Background. Music listening represents a gold standard in the evidence-based holistic nursing practice. However, music listening is seldom involved in orthopedic postoperative settings, and only a few related studies can be retrieved in literature. Purpose. The aim was to assess the effects of music during the orthopedic postoperative period, when patients frequently report pain and anxiety. Methods. A randomized controlled trial on 56 patients, equally divided in an experimental group treated with music and a control group in standard care, was conducted during their first-day of recovery from orthopedic surgery. The primary outcome was the pain level assessed with the VAS scale and the Short Form-McGill Pain Questionnaire. Secondary outcomes assessed were anxiety level, blood pressure, heart and respiratory rates and oxygen saturation. Following surgery, when clinically stabilized and soon after their return to their ward room, patients listened to music from a personal programmed playlist using their smartphones for 30 minutes. Results . In music group, the Short Form-McGill Pain Questionnaire score significantly improved in the sensorial dimension (p=0.006) and in the affective dimension (p=0.02). Patients showed a pleasant experience in listening to music (90%), found it useful in coping with pain (64%) and improving mood (86%). Conclusions. Music in the orthopedic post-surgical period induced significant improvement in pain relief, representing a useful complementary intervention to drug treatment. Music listening could be a safe treatment, inexpensive and simple to manage by nurses in orthopedic postoperative settings.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".