1551 Improving Safety of Vascular Catheter Insertion in High-Risk Newborns through Standardized Teaching
Bibliographic record
Abstract
Background Umbilical and percutaneous central catheter placement in high-risk newborns are common NICU procedures with high complication rates, particularly when inserter experience varies. We developed, implemented and evaluated a standardized catheter insertion training program for NICU clinicians to improve patient safety. Methods Seventy-one clinicians were surveyed to assess learning needs. Using the results, a program that included a manual, didactic seminars, self-study electronic module, pocket cards and low-fidelity simulation for practice and feedback was developed. Effectiveness was assessed with pre- and post-training multiple choice knowledge tests and Xray quiz focused on recognition and management of catheter malposition, plus a post-training simulation performance test. Malposition of catheters inserted in the NICU was the primary outcome. Results Real-life practice and simulation were the highest rated teaching methods. Seventy-six clinicians completed at least one program component over 3 months. Post-training knowledge scores (65±11% vs 85±9%, n=65, mean±sd) and Xray scores(59±13% vs 69±16%, n=60) improved significantly compared to pre-training (p<0.01). Performance checklist score was 88±8%. Learner satisfaction was high. Catheter malposition rate decreased from 56% (n=292) to 36% (n=374) (p>0.05). Conclusions A standardized training program resulted in improved knowledge and recognition of catheter malposition but not a significant decrease in malposition rate in the NICU.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".