Corneal tissue engineering: From research to industry, quality of life impact, and Latin American ophthalmologists' perspectives
Bibliographic record
Abstract
<ns3:p>Background Tissue engineering research aims to address the global shortage of donated corneal tissue, yet challenges persist in clinical translation. This study assesses the pathway from basic research to clinical adoption in corneal tissue engineering. Methods Bibliometric and patent analyses were conducted using the Web of Science-Core Collection and Lens databases to identify top authors, countries, journals, publication trends, inventors, patent statuses, and affiliated companies. A quality-adjusted life year (QALY) analysis compared engineered corneal endothelium to full keratoplasty. A pilot study surveyed thirty ophthalmologist surgeons from eight Latin American countries. Results A strong upward publication trend (R2 = 0.89, p = 1.53x10^-9) in corneal endothelium engineering was observed over the past decade, led by the USA, China, and Japan. Among 614 research papers, 26 patents and 10 companies were identified. Engineered corneal endothelium showed a QALY gain of 0.74 versus 0.07 of corneal transplants. Most survey respondents (97%) expressed interest in adopting engineered corneal endothelium for transplantation if affordability, biocompatibility, and functionality were assured. Conclusions While tissue engineering offers promise in alleviating corneal scarcity, a significant gap remains between scientific advancements and clinical adoption, presenting “death valleys.” Addressing this requires more efficient navigation of the interplay between scientific progress, technology adoption, and clinical practice.</ns3:p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".