Effect of platelet concentration on clinical improvement in treatment of early stage-knee osteoarthritis with platelet-rich plasma concentrations
Bibliographic record
Abstract
[Purpose] To compare two platelet-rich plasma kits with different platelet concentrations for treatment of knee osteoarthritis. [Subjects and Methods] Male and female patients with knee osteoarthritis who had confirmed diagnosis with X-ray and magnetic resonance imaging were included in this retrospective study. Eligible patients were divided into two groups: Group I, which received platelet-rich plasma kit I, and Group II, which received platelet-rich plasma kit II. Platelet concentrations of both kits were measured by manual counting. For each group, platelet-rich plasma kit was injected twice with a one-month interval between injections. The Western Ontario and McMaster Universities Osteoarthritis Index and the Visual Analog Scale were applied for clinical evaluation before the first injection and one, three and six months after the second injection. [Results] Kits I and II contained 1,000,000 and 3,000,000 platelets/µl respectively. In both groups, initial Western Ontario and McMaster Universities Osteoarthritis Index and Visual Analog Scale scores were significantly higher compared to the latter evaluations. However, no significant difference was observed between groups in terms of clinical evaluations. [Conclusion] Similar clinical results were found in groups receiving different platelet concentrations, therefore, a concentration of 1,000,000 platelet/µl is considered sufficient for pain relief and functional recovery.
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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 | 0.002 |
| 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 | 0.001 | 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".