Summary of the best evidence for the assessment of active pain after surgery in patients with transabdominal hepatectomy
Bibliographic record
Abstract
Objective To summarize the best evidence of active pain evaluation in patients with transabdominal hepatectomy, and to provide basis for nurses to evaluate pain accuracy. Methods According to the "6S" model, guidelines, clinical decisions, expert consensus, and systematic reviews published up until April 1, 2023 in BMJ Best Practice, UpToDate, The Registered Nurses' Association of Ontario, National institute for Health and Care Excellence, Pubmed, the Cochrane Evidence, and China Biology Medicine database were retrieved. The literature quality evaluation was conducted by two researchers with evidence-based training, the literature meeting the criteria was included, and the evidence was extracted. Results A total of 9 literatures were included, including 4 guidelines, 3 expert consensus and 2 systematic reviews, covering 4 topics of principles, timing, tools and content of postoperative active pain assessment, and a total of 18 recommendations were extracted. Conclusion This study summarized the principles, timing, tool selection and content of postoperative active pain assessment in patients with transabdominal hepatectomy, and suggested that the corresponding level of evidence should be adopted in accordance with the clinical characteristics of patients with transabdominal hepatectomy. (目的 总结经腹肝切除患者术后活动性疼痛评估的最佳证据, 为规范临床护士术后活动性疼痛评估提供参考。方法 根据证据资源“6S”模型, 检索BMJ Best Practice、UpToDate、加拿大安大略省注册护士协会网站(RNAO)、英国国家医疗保健优化研究所网站(NICE)、Pubmed、the Cochrane Library及中国生物医学文献数据库(CBM)中的关于经腹肝切除患者术后活动性疼痛评估的证据, 包括指南、临床决策、专家共识、系统评价, 检索时间为建库至2023年4月1日。文献质量评价是由2名经过循证培训的研究人员完成, 对符合标准的文献纳入, 并提取证据。结果 共纳入9篇文献, 包括指南4篇、专家共识3篇、系统评价2篇, 涵盖了术后活动性疼痛评估的原则、时机、工具和内容4个主题, 共提炼出18条推荐意见。结论 该研究总结了经腹部肝切患者术后活动性疼痛评估的原则、时机、工具选择及内容等方面, 建议在实施过程中结合经腹肝切患者临床特点采纳相应级别的证据。)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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".