Peripheral Inserted Central Catheter Use and Related Infections in Clinical Practice: A Literature Update
Bibliographic record
Abstract
Peripherally inserted central catheter (PICC) lines are commonly used by clinicians in daily practice. This strategy has been established the latest years as a common approach in many clinical conditions. Apart from their usefulness, PICC use is related to some complications. Aim of this review is to summarize all relevant publications regarding the PICC-related infection, as sepsis remains a high mortality syndrome. We conducted a PubMed search to identify all relevant publications referring to infective complications after insertion and use of PICC lines in hospitalized adult patients. A great number of publications suggest that PICC lines are widely used in the management of patients. The use of peripheral inserted central lines is related with a few complications, including bloodstream infections. Existing data mainly support their use in specific clinical conditions because of the low infectious rates. Some conflicting data also exist regarding PICC use, due to an unclear benefit from their use compared to other commonly used strategies. Although a number of complications, including bloodstream infections are related with insertion of PICC lines, their use has a promising role and can be used when indicated in a wide variety of clinical conditions, especially in specific categories of patients and prolonged periods.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".