Influence of preoperative Music-guided Resonance Breathing on Anxiety and Stress before Loop Conisation of the Cervix Uteri
Bibliographic record
Abstract
By means of a randomized controlled pilot study the influence of preoperative 15-minute Music-guided resonance breathing (MGRB) on subjective psychophysical strain before and during the surgical procedure of conisation for cervical dysplasia is systematically investigated. Primary research questions were directed at the effects of MGRB on anxiety (measured by STAI and VAS), stress (measured by salivary cortisol) and pain (measured by short-form McGill Pain Questionnaire) before and after surgery compared to treatment as usual(TAU). Secondary research questions addressed whether MGRB increases patient satisfaction with the procedure, any differences in medical outcomes, and MGRB's suitability for day-to-day clinical practice. A total of 22 participants were included in the study and distributed to two groups by means of block-wise pseudorandomization. In line with systematic reviews, significant reduction in the current anxiety level, measured with STAI-S at the beginning of surgery, and a lower cortisol level at the end of the surgery in the intervention group. The other measurements showed no significant outcomes. Results are limited by the small sample size. However, they give reason to recommend a follow-up study with measurement of the respiratory rate as a possible active factor. Additionally, the effectiveness of MGRB should be compared to simply listening to slow tempo music.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".