Effects of Nighttime Noise Reduction by Using Earplugs on the Recovery of Burn Patients after Reconstructive Surgery
Bibliographic record
Abstract
OBJECTIVE: This study aims to investigate the effects of postoperative noise reduction by using earplugs at night on the healing time and scar quality of burn patients who underwent reconstructive surgery. METHODS: This retrospective cohort study analyzed the clinical data of adult burn patients after they underwent reconstructive surgery at the First Affiliated Hospital of Soochow University from January 2022 to June 2023. The patients were divided into the postoperative noise reduction group (n = 66) and the control group (n = 75) based on whether they received postoperative noise reduction. The control group received standard humanized clinical care, while the postoperative noise reduction group received additional noise-reducing care by using earplugs at night. The effects of postoperative noise reduction on wound healing time, scar quality (Vancouver Scar Scale [VSS]), psychological state (Self-rating Anxiety Scale [SAS] and Self-rating Depression Scale [SDS]), quality of life (Abbreviated Burn Specific Health Scale [BSHS-A]) and incidence of complications were compared between the two groups. RESULTS: Compared with the control group, the postoperative noise reduction group demonstrated significant differences in wound healing time (P < 0.001), VSS scores (P = 0.003), SAS scores (P = 0.005), SDS scores (P = 0.003), and BSHS-A scores (P < 0.05). No significant difference was found in the incidence of complications between the two groups (P > 0.05). CONCLUSION: In conclusion, this study provides substantive evidence that postoperative noise reduction positively influences wound healing time, scar quality, quality of life, and psychological state in burn patients after reconstructive surgery. Such insight emphasized the importance of creating a healing-conducive hospital environment for burn patients, integrating noise reduction strategies as a part of holistic nursing practices to optimize recovery outcomes.
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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 | 0.000 | 0.001 |
| 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".