Use of Plasma Rich in Growth Factors for Ocular Surface Disorders: A Systematic Review
Bibliographic record
Abstract
PURPOSE: Ocular surface disorders (OSDs) can severely affect vision and quality of life. Autologous blood products, such as plasma rich in growth factors (PRGF), are recently available to treat OSDs refractory to traditional therapies. This review aims to summarize the efficacy and safety of PRGF in OSDs. METHODS: This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The protocol was established a priori and published on PROSPERO (CRD42024522119). MEDLINE, Embase, and Cochrane Library were searched for primary articles until February 6, 2024. Primary outcomes included slit-lamp examination findings and patient-reported outcomes. Secondary outcomes included visual outcomes and adverse events. Risk of bias was assessed using the Cochrane Risk of Bias and ROBINS-I tools. RESULTS: Twenty-two studies involving 1158 eyes were included. PRGF showed notable improvement in objective and subjective outcomes in OSDs. Comparative studies did not show PRGF to be superior to a standard steroid taper for dry eye disease. However, the use of PRGF was also reported in persistent epithelial defects and corneal ulcerations. In these conditions, there were high rates of complete healing and reduced corneal staining. PRGF has also been reported to improve ocular surface healing and stability when used as an adjunct to refractive and pterygium surgeries. No serious adverse events were reported. CONCLUSIONS: PRGF has potential as an effective treatment of OSDs resistant to traditional therapies with minimal safety concerns. Large randomized controlled trials are needed to better evaluate the role of PRGF within the treatment armamentarium for corneal pathologies.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".