Plasma rico en plaquetas versus proloterapia para el manejo del dolor en la osteoartrosis de la rodilla
Bibliographic record
Abstract
The objective of the research was to determine the effectiveness between joint infiltrations with platelet-rich plasma vs. prolotherapy with hypertonic 50% dextrose solution + local anesthetic: 1% lidocaine, as a strategy for pain management in patients with knee osteoarthritis, Instituto Autónomo Hospital Universitario de Los Andes, January 2022 to March 2023. Method: experimental clinical study type therapeutic trial with two study groups. Sample of 74 cases that met the inclusion criteria, divided into two groups, PRP (30 cases) and PRL (44 cases). Results: female (67.7%), age (62.03 ± 11,016 years), normal BMI (43.5%), overweight (37.1%) and obesity (19.4%). 67.7% indicated some comorbidity, the most frequent being HTN (50.0%). 77.9% attended physical therapy. According to the Kellgren and Lawrence scale, 87.8% were found to have grade I to III osteoarthritis and 12.2% were grade IV. P<0.05 are considered between before and after each treatment, specifically between the assessment of pain according to the Visual Analogue Scale (VAS) and the evaluation of functional capacity according to the Western Ontario Mc Master Universities Osteoarthritis Index (WOMAC) questionnaire between the income and discharge results. No statistical differences were obtained between the results of both treatment groups. Conclusions: it was determined that both treatments were effective in managing pain in knee osteoarthritis, finding an average three times less in the degree of pain according to VAS and improvement in the WOMAC compared to the initial evaluation.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".