Risk factors for mechanical complications of peripherally inserted central catheters in children
Bibliographic record
Abstract
OBJECTIVE: To determine risk factors for mechanical (noninfectious) complications in peripherally inserted central catheters (PICCs) in children. DESIGN: Retrospective cohort study. SETTING: Pediatric tertiary-care center in Nova Scotia, Canada. PATIENTS: Pediatric patients with a first PICC insertion. METHODS: All PICCs inserted between January 2001 until 2016 were included. Age-stratified (neonates vs non-neonates) Fine-Grey competing risk proportional hazard models were used to model the association between each putative risk factor and the time to mechanical complication or removal of the PICC for reasons not related to a mechanical complication. Models were adjusted for confounding variables identified through directed acyclic graphs. RESULTS: Of 3,205 patients with PICCs, 706 had mechanical complications (22% or 14 events/1000 device days). For both neonates and older children, disease group, lumen count, and prior leak were all associated with mechanical complications in the adjusted proportional hazards model. Access vein and prior infection were also associated with mechanical complications for neonates, and age group was associated with mechanical complications among non-neonates. CONCLUSIONS: We have identified several risk factors for mechanical complications in patients with PICCs that will help improve best practices for PICC insertion and care.
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".