The PEG-coated collagen patch Hemopatch® for hemostasis and dural sealing in neurosurgery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it