The Best-Evidence of Cluster Nursing Prevention Strategies for Perioperative Venous Thrombosis in Patients with Gynecological Cancer
Bibliographic record
Abstract
Objective: To comprehensively retrieve, evaluate, and summarize the best evidence of bundle nursing prevention strategies for perioperative venous thrombosis in patients with gynecological cancer. Methods: The National Guideline Library NGC, the Australian JBI Center for Evidence-based Health Care (JBI EBP), the Scottish InterCollege Guidelines Network (SIGN), the Registered Nurses Association of Ontario (RNAO), and the Canadian Clinical Practice Guidelines Network (CMA) were searched by computer INFOBASE), New Zealand Clinical Practice Guidelines Study Group (NZGG), ClinicalKey for Nursing, TRIP Database, Best Practice, Nursing Consult, The Cochrane Library, Pubmed, Chinese Biomedical Literature Database (CBM), Medical Pulse, MJ Best Practice, UpToDate, PubMed, Web of The literature on prevention and treatment strategies of deep vein thrombosis in perioperative patients with gynecological tumors in the Science core database, Wanfang database, CNKI database and other databases were evaluated and extracted by two researchers independently. Results: 12 articles were included, including five guidelines, four expert consensus, and three systematic reviews. Twenty-one best pieces of evidence were summarized from three aspects: before, during, and after surgery. Conclusion: This study summarizes the best evidence of cluster nursing prevention strategies for perioperative venous thrombosis in patients with gynecological cancer, which can provide a basis for clinical medical staff. Keywords: Gynaecological Tumor; Deep Vein Thrombosis; Evidence-Based Nursing; The Perioperative Period; Summary of Evidence.
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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 | 0.013 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".