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Record W4402585899 · doi:10.7759/cureus.69662

Leukocyte-Poor Platelet-Rich Plasma for the Management of Knee Osteoarthritis: A Retrospective Study With 12 Months of Follow-Up

2024· article· en· W4402585899 on OpenAlexaboutno aff
Ashim Gupta, Arun Viswanath, G Hari Kumar

Bibliographic record

VenueCureus · 2024
Typearticle
Languageen
FieldMedicine
TopicPeriodontal Regeneration and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePlatelet-rich plasmaOsteoarthritisRetrospective cohort studyPlateletPhysical therapyInternal medicinePathologyAlternative medicine

Abstract

fetched live from OpenAlex

Introduction The knee, the most frequently affected joint in osteoarthritis (OA), impacts the life quality of millions of individuals globally, resulting in a considerable healthcare burden. Conservative treatments are preferred, turning to surgical intervention when necessary. Nonetheless, these conventional modalities have drawbacks. Recently, the use of regenerative medicine therapies, including autologous peripheral blood-derived orthobiologics (APBOs), such as leukocyte-poor platelet-rich plasma (LP-PRP), has evolved and demonstrated the ability to manage knee OA. The primary objective of this investigation was to evaluate the efficacy of LP-PRP via widely used patient-reported outcome measures (PROMs) in grade I or II (on the Kellgren-Lawrence scale) knee OA patients. The secondary objective was to characterize the formulated LP-PRP and determine the efficiency of the leukodepletion filter used for leukocyte removal and platelet recovery. Methods This investigation was a retrospective analysis of data collected from patients treated at a single center over a period of 15 months. Data from 40 patients included in this study were intra-articularly injected with 3mL of formulated LP-PRP under ultrasound guidance. PROMs questionnaires, including Kujala and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores, were used and responses were documented at baseline and up to 12 months follow-up. The characterization of the formulated LP-PRP and the efficiency of the leukodepletion filter in removing leukocytes and recovering platelets were assessed via complete blood count (CBC) analysis. Results The intra-articular administration of LP-PRP resulted in statistically significant improvements in Kujala and WOMAC scores in patients with Grade I or II OA of the knee at all follow-up time points (four to 12 months) compared to the respective baseline scores. The subgroup analysis showed significant improvements in Kujala and WOMAC scores in both male and female grade I or II knee OA patients with or without comorbidities, including diabetes and/or hypertension. The characterization of formulated PRP showed platelet concentration to be at least 6x compared to the baseline whole blood levels, the absolute platelet count to be at least 5 billion, and total leukocytes, lymphocytes, neutrophils, and RBCs were depleted by over 88%, 82%, 98%, and 98%, respectively. In addition, the utilization of the PuriBlood leukocyte reduction filter (Puriblood Medical Co. Ltd., Baoshan Township, Taiwan) led to the depletion of approximately 93% of leukocytes and the recovery of about 83% of platelets. Conclusions Administration of LP-PRP resulted in significant improvements in pain and function of patients suffering from grade I or II OA of the knee. In addition, the leukodepletion filter used to formulate LP-PRP, successfully resulted in the depletion of leukocytes while recovering the platelets. More sufficiently powered, multi-center, prospective, non-randomized, and randomized controlled trials with long-term follow-up are needed to further establish the effectiveness of this formulation in knee OA patients.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.018
GPT teacher head0.274
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations3
Published2024
Admission routes1
Has abstractyes

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