Nursing Interventions to Prevent Complications in Patients with Peripherally Inserted Central Catheters: A Scoping Review
Bibliographic record
Abstract
Background: A Peripherally Inserted Central Catheter (PICC) is a safe and effective Central Vascular Access Device when properly used. Therefore, it has become an increasingly frequent procedure. Nurses are often the professionals responsible for its insertion, maintenance, and removal. Despite the advantages of this device, it presents risks and possible associated complications. This scoping review aims to identify and analyze nursing interventions to prevent complications in adults with PICC. Methods: The review was conducted according to Joanna Briggs Institute’s scoping review proposal. The electronic databases Pubmed, CINAHL Complete, MEDLINE Complete, Cochrane Central Register of Controlled Trials, Nursing & Allied Health Collection: Comprehensive, Cochrane Database of Systematic Reviews, Cochrane Methodology Register, Library, Information Science & Technology Abstracts, and MedicLatina were consulted in October 2023. Additionally, we searched the websites of the Registered Nurses Association of Ontario and the Canadian Vascular Access Association. We included articles published in English and Portuguese between 2018 and 2023. Results: A total of 170 articles were initially identified. After selecting and analyzing the articles, 13 studies were included. This review identified nursing interventions in adults to prevent PICC-related complications, categorized into five main groups: pre-procedure, during the procedure, post-procedure, maintenance, and team management interventions. Nurses are pivotal in averting PICC complications by employing evidence-based nursing interventions at each process stage. Conclusions: The importance of nursing interventions in enhancing patient safety, improving health outcomes, and informing clinical practice highlights the need for standardized protocols, specialized training, and consistent patient education for PICC care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".