Effects of <scp>Peripherally inserted Central Catheter (PICC)</scp> materials and designs on reduction of <scp>PICC</scp>‐related complications: A systematic review and meta‐analysis
Bibliographic record
Abstract
Studies showed that integrating coating or valve into Peripherally Inserted Central (PICC) can prevent related complications. However, data regarding efficiency were controversial. Therefore, a systematic review was needed to analyse the effect of PICC materials and designs on reduction of PICC-related complications. We searched PubMed, Cochrane library, EMbase, grey literature and referent literature from inception to 5 August 2022. Randomized controlled trials (RCTs) and case-control study were included. Two authors extracted data independently, using a predesigned Excel form, and assessed the quality of included RCTs according to the Cochrane Handbook for Systematic Reviews (V5.1.0), case-control study was assessed by the Newcastle-Ottawa Scale. Data were analysed using Review Manager (v5.3.0). A total of 10 RCTs and one case-control study were included. Meta-analysis results showed that PICC designs reduce the incidence of obstruction, and at the critical value of PICC-associated bloodstream infection, but may have no effects on other complications. Based on the literature reviewed, we can only say PICC new materials did not reflect significant reduction on complications, what's more, the result needs more multicentre, large RCTs to support. We suggested clinicians combine descriptive research and cost-effect analysis to select appropriate PICC materials and designs for patients.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.032 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".