123 Evaluation of a Unique Pediatric Nurse-Inserted PICC Program
Bibliographic record
Abstract
Concerns regarding safety and success of bedside PICC insertions in the pediatric population initially precluded the development of a nurse-inserted PICC program at our pediatric centre. All PICCs were inserted by interventional radiologists (IRs) under fluoroscopic guidance. We initiated a new nurse-inserted PICC program which is a collaboration between PICC nurses and IR. Three nurses participated in the project. Patients who met pre-established selection criteria were selected. All insertions were performed using sterile technique on the fluoroscopy table with IRs available to support the PICC nurse. Veins were accessed visually or by palpation. Final tip position was confirmed in all cases with contrast and fluoroscopy. Additional fluoroscopy was only used if placement difficulties were encountered. All patients were followed prospectively. Ninety-nine patients aged 3–18 years (average 13.4) met the selection criteria. Two patients had a primary insertion by an IR. The remaining 97 patients underwent an insertion attempt by a nurse. Sixty-nine PICCs were successfully placed by a nurse (71.1%), fifteen (15.5%) required minor assistance from a radiologist and thirteen (13.4%) were inserted by a radiologist after an unsuccessful nurse attempt. No insertion complications were noted. Insertion difficulties included difficulty advancing the catheter (19.6%), difficulty cannulating the vein (6.2%), and tip malposition (2.1%). Post-insertion complications occurred in 27.8% of PICCs, and 13.4% required removal prior to the end of therapy. This novel pediatric nurse-inserted PICC program has a high safety profile, high success rate and low post-procedure complication rate.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".