The influence of different types of PICC lines on the incidence of complications:a systematic review
Bibliographic record
Abstract
Objective To evaluate the influence of tip designs,materials and diameters of PICC lines on the incidence of PICC-related complications.Methods The relevant randomzed controlled trials,quasi-randomized controlled trials,case-control studies,cohort studies were searched in the Cochrane Library,JBI Library and the databases of MEDLINE,EMbase,CBM and CNKI.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 and Newcastle-Ottawa Scale.Meta-analysis was conducted by using RevMan 5.1.Results One randomized controlled trial,three quasi-randomized controlled trials,one case-control study and five cohort studies involving 1996 patients were included.The results of meta-analysis showed that the open-end catheter was associated with a significantly higher incidence of catheter occlusion than Groshong catheter [OR=0.36,95%CI(0.19,0.69),P= 0.002].Polyurethane catheters had a significantly higher incidence of complications than silicone catheters [OR=0.24,95% CI(0.07,0.85),P =0.03 ].Conclusion Groshong catheters have an advantage in reducing catheter occlusion than open-end catheters.Catheter maintenance should be crucial when using polyurethane catheters to avoide complications.Minimum inner diameter catheters should be used to reduce incidence of thrombophlebitis.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.015 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".