Effect of Quality Nursing Intervention on the Efficacy of Treating Hypertrophic Burn Scars with Asiaticoside Cream Ointment and Nursing Satisfaction
Bibliographic record
Abstract
Objective. To explore the effect of quality nursing intervention on the efficacy of treating hypertrophic burn scars with asiaticoside cream ointment and nursing satisfaction. Methods. A total of 80 patients with hypertrophic burn scars treated in our hospital from January 2019 to January 2021 were retrospectively analyzed and divided into group A (conventional nursing) and group B (quality nursing) according to the different nursing modes, with 40 cases each. All patients were treated with the asiaticoside cream ointment, and after nursing intervention, the effect of different nursing modes on the patients’ clinical efficacy and nursing satisfaction was scientifically evaluated. Results. No statistical differences in patients’ general information were observed (P>0.05); the overall effective rate of treatment was obviously lower in group A than in group B (77.5% vs 95%, P<0.05); after nursing, the Vancouver Scar Scale (VSS) scores of patients in both groups were significantly lower than before (P<0.05), and the VSS scores after nursing of group B were significantly lower than those of group A (P<0.05); after nursing, the Hamilton Rating Scale for Depression (HAMD) scores and Hamilton Rating Scale for Anxiety (HAMA) scores were obviously lower in group B than in group A (P<0.05); and the overall satisfaction with nursing was significantly higher in group B than in group A (P<0.05). Conclusion. Performing quality nursing for patients with hypertrophic burn scars who accepted the asiaticoside cream ointment treatment can effectively promote clinical efficacy, reduce the negative emotions of patients, and improve the satisfaction with nursing.
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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) | 0.000 | 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".