In Search of the Optimal Processing Technique for Fat Grafting
Bibliographic record
Abstract
OBJECTIVE: Unpredictability in graft retention remains a significant drawback of fat grafting. Processing of fat grafts has been the focus of several studies to improve graft survival. The objective of this study was to systematically review the outcomes of different fat graft processing techniques with the goal of (1) deriving clinically oriented insights and (2) identifying gaps in knowledge to stimulate future research. METHODS: PubMed, EMBASE, and Cochrane Databases were searched to identify studies that compared different fat graft processing techniques. Outcome measures of interest were any subjective or objective measures of fat graft survival or reports of adverse events. RESULTS: A total of 2056 abstracts were generated from the literature searches; 13 studies met the criteria for data extraction and analysis. Processing methods assessed included decantation, washing, gauze filtration, and centrifugation. Each processing method was found to be better than other methods, depending on the outcome measure used to study graft survival. As well, several studies found statistical equipoise in the outcome measures when analyzing the results of the different techniques. Adverse events were rarely reported and did not correlate with any processing method in particular. CONCLUSIONS: No firm concluding recommendation can be made to deem 1 processing technique superior to the others. However, it would seem that techniques, which use a combination of gentle washing and centrifugation, strike the optimal balance of preserving adipocyte viability while removing bulk of the contaminants.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".