Sonographic Detection of Iatrogenic Carotid Artery Guidewires During Internal Jugular Vein Catheterization
Bibliographic record
Abstract
Background: Visualization of the guidewire during internal jugular (IJ) vein catheterization by point of care ultrasound (POCUS) has been recommended for avoiding inadvertent carotid artery dilation. The purpose of this study was to determine the accuracy of POCUS for identifying guidewires inappropriately placed in the carotid artery. Methods: This prospective, observational study involved emergency medicine (EM) residents with varying experience in guidewire visualization. Using an inanimate model, investigators placed guidewires randomly into the carotid artery or IJ vein. Residents, blinded to guidewire location, scanned the model and recorded their findings. The test performance of POCUS for arterially placed guidewires was evaluated through calculation of sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and overall accuracy, using investigator placement as the non-reference standard. Results: Twenty-five residents performed 51 observations. The test performance of POCUS for identifying arterially placed guidewires was sensitivity 95.0% (95%CI = 73.1-99.7%), specificity 96.8% (95%CI = 81.5-99.8%), NPV 96.8% (95%CI = 81.5-99.8%), and PPV 95.0% (95%CI = 73.1-99.7%). The overall accuracy was 96.1% (95%CI = 86.8-98.9%). Residents reported being very confident in their findings in 88.2% of all observations (95%CI = 76.6-94.5%), somewhat confident in 9.8% (95%CI = 4.3-21.0%), and not very confident in 2.0% (95%CI = 0.4-10.3%). No errors occurred among upper-level residents (post-graduate years 2-3) or those reporting >5 prior wire visualizations in live patients. Conclusions: This study is the first to demonstrate that physicians can easily identify misplaced guidewires located in the carotid artery with a high degree of accuracy using POCUS. We recommend routine scanning of the IJ vein and carotid artery prior to vessel dilation to reduce the likelihood of carotid artery injury.
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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 | 0.003 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".