Combined Face and Whole Eye Transplantation: Cadaveric Rehearsals and Feasibility Assessment
Bibliographic record
Abstract
Background: In properly selected patients, combined face and whole eye transplantation (FWET) may offer a more optimal aesthetic and potentially functional outcome while avoiding the complications and stigma of enucleation and prosthetics. This study presents the most comprehensive cadaveric assessment for FWET to date, including rehearsal allograft procurement on a brain-dead donor. Methods: Over a 2-year period, 15 rehearsal dissections were performed on 21 cadavers and one brain-dead donor. After identification of a potential recipient, rehearsals assessed clinical feasibility and enabled operative planning, technical practice, refinement of personalized equipment, and improved communication among team members. Operative techniques are described. Results: Facial allograft procurement closely followed previously described face transplant techniques. Ophthalmic to superficial temporal (O-ST) vessel anastomosis for globe survival was assessed. Craniectomy allowed for maximal optic nerve and ophthalmic vessel pedicle length. Appropriate pedicle length and vessel caliber for O-ST anastomosis was seen. Research procurement demonstrated collateral blood flow to the orbit and surrounding structures from the external carotid system as well as confirmed the feasibility of timely O-ST anastomosis. Personalized cutting guides enabled highly accurate bony inset. Conclusions: This study formalizes an approach to FWET, which is feasible for clinical translation in judiciously selected patients. O-ST anastomosis seems to minimize retinal ischemia time and allow perfusion of the combined allograft on a single external carotid pedicle. Although restoration of vision likely remains out of reach, globe survival is possible.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".