Evaluation of a Unique, Nurse-Inserted, Peripherally Inserted Central Catheter Program
Bibliographic record
Abstract
BACKGROUND: Concerns regarding the safety and success of peripherally inserted central catheters (PICCs) placed at the bedside in the pediatric population initially precluded the development of a nurse-inserted PICC program at our pediatric center. Previously, all PICCs were inserted by interventional radiologists (IRs) with fluoroscopic guidance. A new nurse-inserted PICC program was initiated with collaboration between PICC nurses and IRs. METHODS: Three nurses participated in the project. Patients who met preestablished selection criteria were approached. All insertions were performed with sterile technique on the fluoroscopy table, with IRs available to support the PICC nurse. Veins were accessed visually or through palpation. Final tip position was confirmed in all cases with contrast material administration and fluoroscopy. Additional fluoroscopy was performed only if placement difficulties were encountered. All patients were monitored prospectively. RESULTS: Ninety-nine patients (age: 3-18 years; average age: 13.6 years) met the selection criteria. Two patients underwent primary insertion by an IR. The remaining 97 patients underwent an insertion attempt by a nurse. Sixty-nine PICCs (71.1%) were placed successfully by a nurse, 15 (15.5%) required minor assistance from an IR, and 13 (13.4%) were inserted by an IR 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%). Postinsertion complications occurred for 27.8% of PICCs, and 13.4% required removal before the end of therapy. CONCLUSION: This novel, pediatric nurse-inserted PICC program has a good safety profile, high success rate, and low postprocedural 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".