The PEG-coated collagen patch Hemopatch® for hemostasis and dural sealing in neurosurgery
Bibliographic record
Abstract
Background: Postoperative cerebrospinal fluid (CSF) leakage and bleeding are major postoperative complications that increase healthcare system costs. The use of Hemopatch® Sealing Hemostat has been shown to reduce the incidence of such postoperative complications. This technical report aims to provide neurosurgeons with the best recommendations for the effective use of Hemopatch® as a hemostatic and dural sealant in cranial and spinal procedures. Material and methods: The clinical experiences of 10 neurosurgeons from around the world regarding the use of Hemopatch® were evaluated using an online survey, followed by a hands-on preclinical workshop on adult pigs, which concluded with an in-depth discussion about the use of the patch. Results: The survey results provide an overview of how and when experts use different types of dural repair materials, including decision-making factors. During the workshop, Hemopatch® presented excellent tissue adherence on all evaluated defects. The new configuration of the patch showed improved tissue adherence, less curling of the patch, and easier removal of the gauze used for compression. Experts recommend using patches that overlap the defect for ≥1 cm. When closing defects that do not allow for a dried application site, Hemopatch® can be put on a dry gauze, which can be bent into a U-shape. This allows better targeting of the application site and enables immediate compression upon placement. Conclusion: The results provide information to improve the hands-on use of Hemopatch® as a dural sealant, therefore reducing the risk of postoperative complications such as CSF leaks, eventually reducing healthcare system costs.
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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 | 0.000 | 0.000 |
| 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) | 0.003 | 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".