Effects of Operating Room Noise on Anxiety and Pain in Non-General Anaesthesia Orthopaedic Surgery under Seamless Care and Diversified Health Education
Bibliographic record
Abstract
BACKGROUND: Operating room noise is a potential stressor that can adversely affect patients undergoing surgeries under non-general anaesthesia. This study aims to evaluate the effects of noise control measures in these patients. METHODS: We retrospectively analysed the medical records of 296 patients who underwent non-general anaesthesia orthopaedic surgery between January 2021 and December 2023. Patients were divided into the following two groups according to the treatment received: the operating room noise control (ORNC) group, which used noise-cancelling headphones, and the conventional operating room noise (CORN) group, which did not have noise reduction. We evaluated stress markers (cortisol, epinephrine, norepinephrine); anxiety (State-Trait Anxiety Inventory) and pain perception (Short-Form McGill Pain Questionnaire) before and after surgery. Patient satisfaction was gauged using the Hospital Consumer Assessment of Healthcare Providers and Systems survey. Statistical methods included t-test and Mann-Whitney U test for continuous variables and Chi-square test for categorical variables. RESULTS: Baseline demographics and clinical characteristics, including anxiety and stress indicators, were similar between groups preoperatively. After operation, patients in the ORNC group exhibited significantly lower systolic blood pressure, heart rate, cortisol and catecholamine levels compared with the CORN group (P < 0.05, for all). The ORNC group also had significantly reduced postoperative anxiety and pain scores (P < 0.05) and need for sedative medications (P = 0.002). Additionally, the patient satisfaction was higher in the ORNC group, with more reporting they were 'very satisfied' (37.96% vs. 22.64%, P = 0.009). CONCLUSION: This study systematically evaluates the effectiveness of noise control in non-general anaesthesia orthopaedic surgery. Implementing noise control measures significantly reduces anxiety, pain perception and physiological stress markers, positively impacting the patient's recovery. These findings highlight the importance of auditory environment management as a critical component of comprehensive patient care and provide a basis for setting new standards for improving surgical outcomes and enhancing patient satisfaction.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".