Evaluation of a commercial vascular clip: risk factors and predictors of failure from <i>in vitro</i> studies
Bibliographic record
Abstract
OBJECTIVE: To assess risk factors and predictors of failure of the Hem-o-lok(TM) vascular clip (Weck Closure Systems, Research Triangle Park, NC, USA) using vessels harvested from a porcine model. MATERIALS AND METHODS: Vessels of various diameters were harvested from a porcine model, clipped at 90 degrees or 45 degrees using the Hem-o-lok clip and then cut either flush or with a 1-mm cuff. The vessels were then connected to a burst-pressure device and pressures required to burst the clip or to cause it to leak were measured. RESULTS: The Hem-o-lok clip leaked or burst when the vessel to which it was applied was cut flush. The clip became even more likely to fail if the angle of application of the clip was not at 90 degrees to the vessel surface. CONCLUSION: The Hem-o-lok vascular clip is safe if it is applied at 90 degrees to the vessel surface and, more importantly, if a 1-mm cuff is left between the clip and the point at which the vessel is divided. We would therefore discourage the practice of not leaving this cuff of tissue, in an attempt to maximize vessel length during laparoscopic donor nephrectomy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".