Quantitiative Analysis of Roughness of Catheters by Scanning Electron Microscopy (SEM) and Atomic Force Microscopy (AFM).
Bibliographic record
Abstract
Abstract Thrombosis is a major complication of central venous access devices, and incidence depends on material, diameter, tip position, and tip surface. Catheters are usually cut to the appropriate length for accurate positioning. However, cutting is not recommended as rough surfaces can serve as a nidus for thrombi. This study was done to assess the roughness of the catheter tips as provided by various manufacturers, versus the roughness once cut and handled. Four types of catheters (Groschong, Hickman, Port-a-Cath, and Per Q Cath) were cut by scissors, iris scissors, or scalpel, and handled with debakey forceps, a needle driver, and adsons with or without teeth, to determine the damage created on the catheter. The manufactured tip was compared as a control. Scanning electron microscopy (SEM) was used to do imaging for all samples and roughness and section analysis was quantified using atomic force microscopy (AFM) for the cutting methods. SEM showed that scalpel-cut and manufactured ends appeared smoother relative to those cut with scissors or iris scissors (Fig 1). This complemented the roughness and section analysis by AFM (Fig 2). Catheters handled by debakey equipment and adsons with teeth showed the most roughness, visible as deep holes or a grainy surface when observed by high magnification SEM. Decreasing smoothness of catheters is in the following order: uncut surface, followed by surfaces cut by scalpel, scissors or iris scissors. Handling should be minimized and use of adsons with teeth, needle drivers and debakey forceps avoided, which can leave permanent damage. The least damage appeared to be adsons without teeth. Thus, the cutting of catheters is not recommended since rough surfaces can serve as a thrombotic nidus. Figure Figure
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".