Progress in the application of auto-concentrated growth factor (CGF) in wound repair
Bibliographic record
Abstract
Auto-concentrated growth factor (CGF) constitutes the latest generation of plasma extract, and has high concentrations of growth factors and white blood cells. Due to the continuous variable speed centrifugation used during preparation, the tensile strength of the fibrin is also higher. CGF preparation does not involve the use of animal serum, minimizing the risk of infection and immune rejection. Therefore, it has wide potential applications in various fields of regenerative medicine. This paper summarizes the history behind CGF development, reviews the clinical applications and research progress concerning single CGF therapy and CGF used in combination with other treatments in multiple wound repair, and summarizes its potential value as therapeutic agent. Finally, some constructive suggestions and research perspectives for the application of CGF in wound healing are put forward. The available evidence indicates that CGF can promote the healing of chronic refractory wounds and acute wound, promote the growth of granulation, accelerate the speed and improve the quality of wound healing, reduce scar formation, minimize the need for repeated wound dressing, and ameliorate the pain experienced by patients.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".