Collaboration for global e-learning impact
Bibliographic record
Abstract
Besides the established techniques of pediculed and free tissue transplantations for breast reconstruction, adipose tissue engineering and structural fat grafting are being applied as options for regenerative therapy. While the initial euphoria about the foreseeable realisation of cell-matrix entities of sufficient size, functionality and long-term volume stability for use in humans has diminished somewhat, fat grafting as experienced a renaissance in recent years. One of the decisive factors for the engraftment of the tissue graft generated though tissue engineering is the formation of an adequate vascular network. Improvements of the matrix, which ideally should mimic natural tissue, such as the use of adipose-derived stem cells (ASCs) that can contribute both to adipogenesis and neoangiogenesis represent promising new approaches. In autologous fat grafting, the mixing of adipocytes and cells of the stromal-vascular fraction (SVF) in order to generate the principle of an inductive microenvironment has already been applied successfully in clinical routine. On the basis of the experimental data that demonstrate an interaction of the adipocytes, ASCs and other progenitor cells with breast cancer cells and the insufficient clinical data regarding oncological safety, this procedure should only be used critically. A concluding evaluation will only be possible after long-term clinical studies have provided good results.
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 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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".