Meta-analysis of risk factors for puncture site bleeding after transfemoral artery puncture intervention
Bibliographic record
Abstract
Abstract Objective To identify the risk factors of bleeding at the puncture site after femoral artery puncture intervention by Meta-analysis, and to provide a basis for postoperative evaluation and prevention of bleeding at the puncture site. Methods China National Knowledge Infrastructure (CNKI), Wanfang, VIP, China Biomedical Literature Service (CBM), PubMed, Medline, The Cochrane Library, EMbase and Web of Science were searched from the establishment of the database to October 10, 2022 Observational studies, including cross-sectional studies, case-control studies, and cohort studies, on risk factors for bleeding at the puncture site after transfemoral artery puncture intervention in the Science database. Newcastle-Ottawa scale (NOS) was used to evaluate the quality of the included studies. Finally, RevMan5.3 software was used for meta-analysis of the literature data. Results Eight articles (total sample size 35250 cases) were included, including 1410 patients in postoperative bleeding group and 33840 patients in non-bleeding group. The influencing factors with statistically significant differences by meta-analysis are as follows: Elderly (OR=2.71, 95%CI = 2.17-3.38), female (R=4.26, 95%CI = 1.08-16.89), hypertension (OR=2.48, 95%CI = 1.69-3.63), obesity (OR=2.33, 95%CI = 1.59-3.42), Thrombolytic agents, anticoagulants, OR platelet antagonists were used (OR=2.95, 95%CI = 2.24-3.89), and manual compression was used (OR=6.78, 95%CI = 1.34-34.43). Conclusions The evidence shows that advanced age, female, hypertension, obesity, the use of thrombolytic agents/anticoagulants/platelet antagonists and manual compression are the risk factors of bleeding at the puncture site after femoral artery puncture intervention, which can provide a reference for the risk assessment of bleeding after clinical surgery and the development of preventive measures.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.067 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".