Allogeneic Solid Platelet-Rich Plasma for Persistent Epithelial Neurotrophic Defects: A Protocol and Pilot Study
Bibliographic record
Abstract
PURPOSE: Here, we present the procedure to obtain allogeneic solid platelet-rich plasma (PRP) and its use in a pilot study of patients with persistent neurotrophic epithelial defects. METHODS: We included 4 eyes of 4 patients with persistent neurotrophic epithelial defects unresponsive to other therapies from a single institution. PRP and thrombin were produced by the Department of Transfusion Medicine from healthy blood donors. PRP was activated in its solid form in the operating room with addition of thrombin and calcium gluconate 10% and applied on the cornea with fibrin glue and soft contact lens. Corneal healing time, corneal esthesiometry, visual acuity, Oxford staining score, Ocular Surface Disease Index questionnaire, and Schirmer I test were recorded. Anterior segment optical coherence tomography and in vivo confocal microscopy were also evaluated over the 4-month follow-up period. RESULTS: The persistent epithelial defect healed in all patients in the first 10 days. During the follow-up, there was an absence of recurrences. For all patients, there was a reduction in Ocular Surface Disease Index questionnaire score (case 1: -55 points, -73.3%; case 2: -26.3 points, -58.4%; case 3: -56 points, -69.1%; case 4: -20 points, -26.6%; mean reduction: 39.3 points, 56.85%) and Oxford staining score (case 1, 2, and 3: 3 points decrease; case 4: 2 points decrease; mean reduction: -2.75 points). CONCLUSIONS: Allogeneic solid PRP in combination with fibrin glue may facilitate wound healing in neurotrophic persistent epithelial defects. Further prospective studies are needed to quantify its efficacy.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".