Autologous Platelet-Rich Plasma Applications in Chronic Pain Medicine: Establishing a Framework for Future Research - A Narrative Review.
Bibliographic record
Abstract
BACKGROUND: During the last decades, platelet-rich plasma has been studied for the treatment of multiple chronic pain conditions, in addition to being employed in the enhancement of healing after tissue injury. OBJECTIVE: To establish a framework for future research regarding the utilization of platelet-rich plasma in the treatment of chronic tissue injuries. METHODS: Preclinical and clinical studies from 2000-2020 relevant to applications of platelet-rich plasma for the treatment of chronic pain conditions were extracted from PubMed and Medline databases. The studies were analyzed on the basis of the study population, type of intervention, method of platelet-rich plasma preparation, the number of treatments administered, the timeframe of injections, and clinical outcomes. RESULTS: Although several preclinical studies and double-blind, randomized trials have shown promising results in the application of platelet-rich plasma for the treatment of multiple chronic pain conditions, various studies have also reported controversial results. Additionally, the methods employed for obtaining the platelet-rich plasma have not been standardized between studies, resulting in different concentrations of blood components between the preparations utilized. Moreover, differences between studies were also found regarding the number of injections administered per treatment. CONCLUSIONS: Future research addressing the utilization of platelet-rich plasma in the treatment of chronic pain conditions should focus on shedding light on the following major questions: a) Is there a dose-effect relation between the platelet count and the clinical efficacy of the preparation?; b) What pathology determinants should be considered when selecting between leukocyte-enriched and leukocyte-depleted concentrates?; c) What is the role of platelet activation methods on the clinical efficacy of platelet-rich plasma?; d) Is there an optimal number of injections and time frame for application of multiple injection treatment cycles?; e) Does the addition of local anesthetics affect the clinical efficacy of platelet-rich plasma?; and f) Is there potential for future platelet-rich plasma applications for the treatment of neuropathic pain of peripheral origin?
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".