Effect of personalized musical intervention on burden of care in dental implant surgery: A pilot randomized controlled trial
Bibliographic record
Abstract
OBJECTIVE: To explore a personalized musical intervention's effect on burden of care during dental implants placement. METHODS: Randomized Controlled Trial in 24 dental implant surgery patients. A personalized music intervention (Music Care© application) or an audiobook control condition was administered. Burden of care (a composite outcome including self-reported anxiety, pain, and dissatisfaction felt during surgery), expected pain prior to surgery, pre- and post-surgery affect, memory of pain felt during surgery, and participants' emotional judgments of the music and audiobook listening were assessed. RESULTS: The personalized music intervention significantly reduced the burden of care for dental implant surgery (p = 0.02; d = 1.07). Both groups reported positive affect after surgery, but the music group felt better. The pain remembered after seven postoperative days was significantly lower in the music group (p = 0.02). Participants judged the music listened to during surgery as more relaxing and pleasant than the audiobook (p = 0.002 and p = 0.001, respectively). CONCLUSIONS: Personalized music intervention could be effective in decreasing patients' burden of care during dental implant surgery. These results need to be confirmed by a rigorous randomized control trial. CLINICAL SIGNIFICANCE: The burden of care associated with the pain and anxiety experienced during dental implant surgery can be reduced using a personalized and standardized music intervention. This approach may provide a simple complementary approach to improve surgical care in various 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".