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Record W2277356941 · doi:10.1007/s11999-015-4207-z

Cochrane in CORR ®: Platelet-rich Therapies for Musculoskeletal Soft Tissue Injuries (Review)

2015· review· en· W2277356941 on OpenAlexaff
Moin Khan, Asheesh Bedi

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2015
Typereview
Languageen
FieldMedicine
TopicPeriodontal Regeneration and Treatments
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineRandomized controlled trialPlaceboPlatelet-rich plasmaDry needlingClinical trialSports medicinePsychological interventionSoft tissuePhysical therapyAutologous bloodIntensive care medicineSurgeryInternal medicinePathologyPlateletAlternative medicine

Abstract

fetched live from OpenAlex

Importance of the Topic Soft tissue injuries represent more than 50% of all musculoskeletal injuries reported each year in the United States [8]. The diagnosis and management of such injuries represent a substantial financial burden, estimated at more than USD 15.8 billion annually [6]. The use of autologous blood concentrates, particularly platelet-rich plasma (PRP), has exponentially grown as a result of significant media attention and use among high-level athletes [9]. While originally used to manage dermatologic and oromaxillofacial conditions, musculoskeletal applications related to bone and soft tissue injury have become widespread [9]. The market for PRP was valued at USD 45 million in 2009 and is expected to be worth more than USD 120 million by 2016 [7]. PRP is defined as a sample of autologous blood with supraphysiological concentrations of platelets [2]. Once activated, platelets release bioactive proteins and growth factors that are thought to aid and promote healing [2], but there is substantial controversy regarding their efficacy [3]. Rigorous evaluation of the available evidence can inform clinicians regarding optimal treatment options for patients. This Cochrane review evaluated all randomized and quasi-randomized controlled trials (19 trials, 1088 patients), of PRP versus placebo, autologous whole blood, dry needling, or no PRP therapy across eight clinical indications. Upon Closer Inspection Heterogeneity can be qualified as either clinical or methodological. The former is defined as variability among studies with regards to participants, interventions, and outcomes and the latter relates to variability in study design and risk of bias [1]. The presence of either of these can result in variability in the intervention effect across studies beyond that which might be expected due to chance alone, which is known as statistical heterogeneity [1]. High-quality meta-analysis should present and evaluate the ways in which results differ between studies. The reader can then judge and explore the reasons for variance in the results and the degree to which the difference influences his or her ability to use the conclusions derived from data pooling [5]. Such exploration and direction of anticipated effect should be specified a priori to reduce the risk of potentially arriving at spurious correlations [10]. A major confounding factor identified in this review of PRP is the heterogeneity of the included studies, particularly with chronicity of injuries, timing of interventions, outcomes reported, and most importantly, a lack of standardization in the application of PRP. More than 40 commercial PRP systems are available and each product may contain differing concentrations of platelets, leukocytes, and growth factors [2]. Included studies varied in the timespan between PRP preparation and delivery, method of delivery (image, arthroscope, direct vision, or no guidance), number of PRP applications, and postoperative cointerventions [4]. Variability in the intervention, as identified by the study authors, may bias the results (eg, comparing preparations with high platelet concentrations versus those with low concentrations). Overall, the evidence was considered low quality, given the uncertainty related to estimates of effect, according to the GRADE approach. Selective reporting was also identified by the study authors as a potential bias in this review. Of the 19 included trials, 11 did not provide a priori protocol or trial registration details for the study, which may bias results in favor of the intervention. Research transparency is improved through publication of such details and allows for identification of selective reporting such as adverse events or surrogate outcomes, which may not be clinically relevant. Take-home Messages This Cochrane systematic review and meta-analysis found no benefit attributable to PRP for short-, medium-, or long-term function. Short-term improvements in pain were identified but the effect sizes were small, and unlikely to be clinically important. These results are similar to other recent systematic reviews and meta-analyses on this subject [9]. This review of best available evidence adds to our understanding that PRP use for musculoskeletal soft tissue injuries is currently unsupported. This review highlights the difficulty with assessment of the efficacy of PRP interventions in orthopaedics and leaves open the possibility that indeed they are not effective. Factors include the lack of standardization and substantial variations in the concentration of platelets and growth factors among available commercial PRP systems [2]. Furthermore, the most efficacious platelet concentrations are not known, dose-response curves are not linear, saturation effects have been described, and the ideal timing of intervention and elution kinetics of growth factors require further evaluation [2]. Current evidence is not sufficient to conclude that PRP provides clear clinical benefit and augmentation of soft tissue healing. Further research through large methodologically rigorous trials with standardized PRP preparations are required to improve understanding related to indications for PRP.

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)0.003
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.033
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.004
Bibliometrics0.0100.010
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0330.003

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 head0.197
GPT teacher head0.558
Teacher spread0.361 · 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 designSystematic review
Domainnot available
GenreReview

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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Citations24
Published2015
Admission routes1
Has abstractyes

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