Towards Precision Ophthalmology: The Role of 3D Printing and Bioprinting in Oculoplastic Surgery, Retinal, Corneal, and Glaucoma Treatment
Bibliographic record
Abstract
In the forefront of ophthalmic innovation, biomimetic 3D printing and bioprinting technologies are redefining patient-specific therapeutic strategies. This critical review systematically evaluates their application spectrum, spanning oculoplastic reconstruction, retinal tissue engineering, corneal transplantation, and targeted glaucoma treatments. It highlights the intricacies of these technologies, including the fundamental principles, advanced materials, and bioinks that facilitate the replication of ocular tissue architecture. The synthesis of primary studies from 2014 to 2023 provides a rigorous analysis of their evolution and current clinical implications. This review is unique in its holistic approach, juxtaposing the scientific underpinnings with clinical realities, thereby delineating the advantages over conventional modalities, and identifying translational barriers. It elucidates persistent knowledge deficits and outlines future research directions. It ultimately accentuates the imperative for multidisciplinary collaboration to enhance the clinical integration of these biotechnologies, culminating in a paradigm shift towards individualized ophthalmic care.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".