Efficacy of aspirin in preventing peripherally inserted central venous catheter-related thrombosis: a systematic review
Bibliographic record
Abstract
Objective To evaluate the effect of aspirin in preventing peripherally inserted central venous catheter (PICC) -related thrombosis by Cochrane systematic review method. Methods The relevant randomized controlled trials, quasi-randomized controlled trials and case-control study were searched in the Wang Fang Databases, VIP, CNKI, CINAHL, Elsevier, Cochrane Database of Systematic Reviews, EBSCO and Pub Med. The studies were selected according to the inclusion and exclusion criteria by two reviewers, and the quality of included studies was evaluated according to Cochrane Handbook 5.1.0 and Newscastle-Ottawa scale. Meta-analysis was conducted by using RevMan5.0. Results A total of 9 articles included three randomized controlled trials, five quasi-randomized controlled trials and one case-control study involving 3 064 patients. The results of Meta-analysis showed that the control group had a significantly higher incidence of thrombosis than experimental group (OR=3.66, 95%CI=0.42-0.77, P=0.000 2) ; control group had a significantly higher incidence of thrombosis within one week than experimental group (OR=2.99, 95%CI=0.14-0.67, P=0.003) ; control group had a significantly higher incidence of unplanned removal than experimental group (OR=2.63, 95%CI=0.04-0.63, P=0.008) . Control group had a significantly higher incidence of phlebitis than experimental group (OR=4.47, 95%CI=0.16-0.49, P<0.05) . Conclusions Aspirin can be used as an efficient way to prevent PICC associated thrombosis, while the side effect should be closely observed. Key words: Aspirin; Thrombosis; Peripherally inserted central venous catheters; Meta-analysis
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".