Is Platelet-Rich Plasma Effective in Enhancing Spinal Fusion? Systematic Overview of Overlapping Meta-Analyses
Bibliographic record
Abstract
Study Design:Systematic review.Objectives:We performed this systematic overview on overlapping meta-analyses that analyzed the role of platelet-rich plasma(PRP) in enhancing spinal fusion and identify which study provides the current best evidence on the topic and generate recommendations for the same.Materials and Methods:We conducted independent and duplicate electronic database searches in PubMed, Web of Science, Embase, Cochrane Database of Systematic Reviews, and Database of Abstracts of Reviews of Effects till October-2020 for meta-analyses that analyzed the role of PRP in spinal fusion procedures. Methodological quality assessment was made using Oxford Levels of Evidence, AMSTAR scoring, and AMSTAR 2 grades. We then utilized the Jadad decision algorithm to identify the study with highest quality to represent the current best evidence to generate recommendations.Results:3 meta-analyses fulfilling the eligibility criteria were included. The AMSTAR scores of included studies varied from 5-8(mean:6.3) and all included studies had critically low reliability in their summary of results due to their methodological flaws according to AMSTAR 2 grades. The current best evidence showed that utilization of PRP was not associated with significant improvement in patient-reported outcomes such as Visual Analog Score for pain compared to the standard fusion procedure. Moreover, PRP was found to be associated with lower fusion rates.Conclusion:Based on this systematic overview, the effectiveness of PRP as a biological agent in augmenting spinal fusion is limited. Current evidence does not support the use of PRP as an adjuvant to enhance spinal fusion.
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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 | 0.030 | 0.077 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.039 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".