Early results of intra-articular micro-fragmented lipoaspirate treatment in patients with late stages knee osteoarthritis: a prospective study
Bibliographic record
Abstract
AIM: To analyze clinical and functional effects of intra-articular injection of autologous micro-fragmented lipoaspirate (MLA) in patients with late stage knee osteoarthritis (KOA). Secondary aims included classifying cell types contributing to the treatment effect, performing detailed MRI-based classification of KOA, and elucidating the predictors for functional outcomes. METHODS: This prospective, non-randomized study was conducted from June 2016 to February 2018 and enrolled 20 patients with late stage symptomatic KOA (Kellgren Lawrence grade III, n=4; and IV, n=16) who received an intra-articular injection of autologous MLA in the index knee joint. At baseline radiological KOA grade and MRI were assessed in order to classify the morphology of KOA changes. Stromal vascular fraction cells obtained from MLA samples were stained with antibodies specific for cell surface markers. Patients were evaluated at baseline and 12-months after treatment with visual analog scale (VAS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and Knee Injury and Osteoarthritis Outcome Score (KOOS). RESULTS: Three patients (15%) received a total knee replacement and were not followed up completely. Seventeen patients (85%) showed a substantial pattern of KOOS and WOMAC improvement, significant in all accounts. KOOS score improved from 46 to 176% when compared with baseline, WOMAC decreased from 40 to 45%, while VAS rating decreased from 54% to 82% (all P values were <0.001). MLA contained endothelial progenitor cells, pericytes, and supra-adventitial adipose stromal cells as most abundant cell phenotypes. CONCLUSION: This study is among the first to show a positive effect of MLA on patients with late stages KOA.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".