Bibliographic record
Abstract
Blood plays an essential role in the human body. Hemorrhage is a critical cause of both military and civilian casualties. The human body has its own hemostatic mechanism that involves complex processes and has limited capacity. However, in emergency situations such as battlefields and hospitals, when the hemostatic mechanism of the human body itself cannot stop bleeding effectively, hemostatic materials are needed for saving lives. In this review, the hemostatic mechanisms and performance of the most commonly used hemostatic materials, (including fibrin, collagen, zeolite, gelatin, alginate, chitosan, cellulose and cyanoacrylate) and the commercial wound dressings based on these materials, will be discussed. These materials may have limitations, such as poor tissue adhesion, risk of infection and exothermic reactions, that may lessen their hemostatic efficacy and cause secondary injuries. High-performance hemostatic materials, therefore, have been designed and developed to improve hemostatic efficiency in clinical use. In this review, hemostatic materials with advanced performances, such as antibacterial capacity, superhydrophobicity/superhydrophilicity, superelasticity, high porosity and/or biomimicry, will be introduced. Future prospects of hemostatic materials will also be discussed in this review.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".