Meta-analysis of the traditional Chinese medicine care model in relieving postoperative pain in patients with anorectal diseases
Bibliographic record
Abstract
Objective: To explore the effect of traditional Chinese medicine (TCM) nursing on relieving postoperative pain in patients with anorectal diseases. Method: Three English and three Chinese databases (PubMed, Embase, the Cochrane Library, the China National Knowledge Internet, Wanfang Data and the China Science and Technology Journal Database) were systematically searched for case-controlled or prospective studies evaluating the impact of TCM nursing on postoperative pain in patients with anorectal diseases from the date of library construction until June 20, 2022. The Newcastle-Ottawa Scale was adopted to evaluate the quality of the observational studies. The effect of TCM care on pain based on the Visual Analogue Scale (VAS), the effective pain relief rate, the wound healing time and the length of hospital stay were systematically analysed. Result: After a systematic search and screening, a total of 15 documents were included in this study. The systematic evaluation showed that TCM care reduced the VAS score (mean difference (MD): 1.15（95 % Confidence Interval (CI): 1.96, -1.06; P < 0.00001) compared with conventional postoperative care methods. As TCM nursing time increased, there was a trend towards decreased VAS scores. Furthermore, TCM care was effective in providing pain relief (OR: 4.78; 95 % CI: 2.93,7.79; P < 0.00001) and reducing wound healing time (MD: 4.44; 95 % CI: 5.60, -3.27; P < 0.00001) and length of hospital stay (MD: 4.87; 95 % CI: 5.93, -3.82; P < 0.00001). Conclusion: Traditional Chinese medicine nursing has a positive effect on the postoperative clinical results of patients with anorectal diseases, especially in relieving postoperative pain. The effect of traditional Chinese medicine nursing in relieving short-term postoperative pain in patients with anorectal diseases is obvious. However, there is no uniform standard for TCM nursing projects, which may lead to heterogeneity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".