The Use of Ultrasound to Improve Axillary Vein Access and Minimize Complications during Pacemaker Implantation
Bibliographic record
Abstract
BACKGROUND: The Agency for Healthcare Research and Quality in the United States recommends the use of ultrasound (US) for central venous access to improve patient outcomes. However, in a recent publication, US is still underutilized for axillary vein access during pacemaker implantation. OBJECTIVE: We sought to describe a technique for US-guided axillary vein access during pacemaker implantation and to report complication rates and success rate. METHODS: Retrospective data collection included success rate and complications on all pacemaker implants by one operator since implementing the systematic use of US at our institution, from November 2012 to January 2015. For the last 59 cases, data were collected prospectively to include time of venous access and number of attempts. RESULTS: A total of 403 consecutive patients were included in the analysis. Two leads were implanted in 255 cases and one lead was implanted in 148 cases. The rate of successful US-guided access was 99.25%. There were no access-related complications. The average number of venipuncture attempts was 1.18 per patient. The average time to obtain venous access was 2.24 minutes including the time to apply the sterile US sleeve. CONCLUSION: The described technique has the potential to improve the success rate of axillary vein access and minimize complications during pacemaker implantation.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".