The use of <scp>intra‐articular</scp> platelet rich plasma for the symptomatic management of osteoarthritis of the knee: a pilot study
Bibliographic record
Abstract
BACKGROUND: Osteoarthritis of the knee is a chronic inflammatory condition resulting in significant patient disability, with intra-articular platelet rich plasma (PRP) injections having shown potential to improve symptomatic outcomes. This retrospective cohort pilot study aimed to observe whether PRP injections were beneficial in the symptomatic management of knee osteoarthritis in an Australian population, based on patient reported outcomes. An additional aim was to observe for an association between the number of injections and patient characteristics, such as body mass index, age, sex and radiologically determined severity of the disease. METHODS: The cohort was drawn from those who attended Ballarat Orthopaedic and Sports Medicine for PRP injections and who had completed the appropriate pre- and post-injection Western Ontario and McMaster Universities Arthritis Index (WOMAC) questionnaire. WOMAC scores were analysed to observe for any difference following a course of PRP injections. RESULTS: The data suggest that the use of PRP improved patient reported WOMAC scores. Additionally, it was shown that two injections had a greater effect than one injection, with a third injection providing no further benefit. Finally, there was an association with lower WOMAC scores post PRP therapy amongst male participants compared to female participants. CONCLUSION: These results suggest two PRP injections are optimal for the symptomatic management of knee osteoarthritis, identifying a need for further prospective research in this Australian population.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".