Failed attempts and improvement strategies in peripheral intravenous catheterization
Bibliographic record
Abstract
BACKGROUND: Access to peripheral veins is necessary for sample collection, transfusion and infusion of fluids or medications. The peripheral intravenous catheterization (PIVC) procedure is the introduction of a short catheter into a peripheral vein and can be problematic, leading to multiple failed attempts. PURPOSE: To analyze scientific literature regarding difficulties in establishing peripheral intravenous access and improvement strategies. METHOD: A literature search was undertaken and secondary references were retrieved from the papers obtained from the initial search. A total of 128 papers published from 1975 to 2011 were reviewed. RESULTS: The first attempt of PIVC fails in 12-26% of adults and 24-54% of children. Factors associated with the currently utilized PIVC success include: (1) patient's characteristics such as age, gender, race, weight/BMI, co-existing medical conditions and skin/vein characteristics, (2) procedure related factors such as the insertion site and catheter caliber, and (3) the operator's expertise. Strategies to improve PIVC success include: (1) bedside techniques such as venodilation, vascular visualization and vein entry indication, (2) pain management and (3) engagement of expert health care providers. CONCLUSION: Bedside techniques have shown more improvement in PIVC success rates as opposed to pain management. Expert health care providers have shown higher performance levels with regard to the difficult cases of PIVC.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".