Best evidence summary of the acute cardiac tamponade care practice
Bibliographic record
Abstract
Objective To screen the evidence related to acute pericardial tamponade nursing and summarize the best evidence applied in nursing practice. Methods To establish the evidence-based questions according to the PIPOST model, perform computer searches of UpToDate, JBI Evidence-Based Health Care Center of Australia, National Guideline Clearinghouse of USA, NGC), Registered Nurses Association of Ontario (RNAO), Cochrane Library, PubMed, Embase, CNKI, Wanfang Database. The search period is from January 1, 2012 to October 31, 2022. The quality of the selected literatures was evaluated by two researchers respectively, and the literatures meeting the criteria were included and the evidence was summarized. Results Twelve literatures were included, including 2 guidelines, 1 expert consensus, 1 evidence summary, 4 clinical decisions, and 4 case series studies. Sixteen pieces of evidence were formed, including two themes: early identification and emergency care. Conclusion This study summarized the best evidence of acute cardiac tamponade nursing, and provided evidence-based evidence for improving the success rate of rescue and nursing quality of patients with cardiac tamponade. (目的 筛选急性心包填塞护理的相关证据, 并总结应用于护理实践的最佳证据。方法 根据PIPOST模式确立循证问题, 计算机检索UpToDate、澳大利亚JBI循证卫生保健中心、美国国立指南库(NGC)、加拿大安大略注册护士协会网站(RNAO)、Cochrane Library、PubMed、Embase、中国知网(CNKI)、万方数据库。检索时限为2012年1月1日至2022年10月31日。由2 名研究者分别对筛选后的文献进行质量评价, 对符合标准的文献纳入并进行证据的总结。结果 最终纳入文献12篇, 包括指南2篇, 专家共识1篇, 证据总结1篇, 临床决策4篇, 病例系列研究4篇。形成16条证据, 包括2个主题: 早期识别、急救护理。结论 本研究总结了急性心包填塞护理的最佳证据, 为提高心包填塞患者的抢救成功率及护理质量提供循证依据。)
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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 | 0.026 | 0.134 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.034 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".