Efeitos das células-tronco mesenquimais no tratamento da osteoartrite de joelho: Um relato de caso no Sistema Único de Saúde do Brasil
Bibliographic record
Abstract
Osteoarthritis (OA) can incapacitate the individual to perform their activities of daily living due to pain. This is an important public health issue that worsens worldwide and in Brazil, since the population goes through an aging process, and has caused increased public spending on the monitoring and maintenance of treatments that can last for years and still not be resolutive. Thus, the search for innovative and effective therapies that can reduce costs becomes necessary. In this context, the present study reports the first application of cell therapy with adipose-derived stem cells in the treatment of cases of OA that are refractory to the conservative treatment, performed in the Brazilian Unified Health System (Sistema Único de Saúde, SUS). The evaluation was performed with the application of the Visual Analog Scale (VAS), the Short Form Health Survey (SF-36) and the Western Ontario and McMaster Universities (WOMAC), specifics for OA evaluation, and also an analysis of the synovial fluid (inflammatory cytokines). The cell therapy improved the scores on the WOMAC, SF-36 and EVA, and reduced the inflammatory process. We observed a decrease of 0.73x in the TNF, of 0,71x in IL-1b, of 0,68x in IL-8, and of 0,70x in IL-10. For IL-6, an increase of 1,48x was observed. Therefore, this cell therapy can be considered promising in aiding the management of this disease, since it improved the patient's pain, decrease inflammatory markers, and enabled the return to activities of daily living, which resulted in an improvement in their quality of life.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".