Platelet-rich plasma administering in osteoarthrosis treatment
Bibliographic record
Abstract
Background — According to the data from the published sources worldwide, about 10% of the world population suffers from osteoarthrosis. While searching for novel methods of osteoarthrosis treatment, we proposed administering intra-articular injections of autologous platelet-rich plasma. The goal of this clinical study was to evaluate the effectiveness of autologous platelet-rich plasma injections in osteoarthrosis patient treatment. Methods — This open-label parallel-group study was a pilot randomized controlled trial. An analysis of the treatment outcomes for 128 patients with knee joint arthrosis was performed. All patients were split among three groups, and subjects in two of those were receiving conventional treatment. Clinical efficacy was calculated from pain intensity, determined using Visual Analog Scale, Lequesne index and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Inflammatory response intensity was assessed by superoxide dismutase content and erythrocyte catalase activity. Results — It was demonstrated that administering platelet-rich plasma allowed achieving tangible clinical and laboratory results without any side effects and with very few contraindications. The latter included, for instance, blocking inflammatory process in the joints, thereby inhibiting the oxidative stress, which is the most pathogenetically substantiated treatment of osteoarthrosis. Conclusion — Thus, we have discovered that platelet-rich plasma injections had apparent anti-inflammatory and pain-relieving effects, along with inhibiting action of destructive processes in the cartilaginous tissue, hence improving life quality of the patients with osteoarthrosis.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".