Cutting Peripherally Inserted Central Catheters May Lead to Increased Rates of Catheter-Related Deep Vein Thrombosis
Bibliographic record
Abstract
The purpose of this study was to determine whether an association exists between cutting or trimming peripherally inserted central catheters (PICCs) and the development of deep vein thromboses (DVTs). An observational, retrospective study was conducted on 634 patients who had a PICC inserted between 2011 and 2012. Patients who had a reverse-taper PICC inserted were assigned into 1 of 2 groups. The first group included patients with a reverse-taper PICC that was cut/trimmed (PC) before insertion (n = 224). The second group was made up of patients whose PICC was not cut/trimmed (PNC) before insertion (n = 410). All PICC-associated DVTs were confirmed by a positive venous Doppler result and recorded. A statistically significant difference (P < .001) was found between patients in the PC group who developed a DVT (9.82%) and patients in the PNC group in which PICCs were not trimmed (1.95%). There is evidence to suggest that altering the reverse-taper PICC by cutting or trimming the tip before insertion may be associated with increased DVTs. Further study is required to determine whether PICCs should be reduced in length or whether there is an appropriate method of trimming the catheter to ensure its stability after insertion.
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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 | 0.009 |
| 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 | 0.001 |
| 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".