Bibliographic record
Abstract
Corneal endothelial diseases comprise a spectrum of conditions that critically affect the health and transparency of the cornea, posing unique challenges for ophthalmologists. The most prevalent among these is Fuchs’ endothelial corneal dystrophy (FECD), which accounts for approximately 39% of all corneal transplants globally. Bullous keratopathy (BK) can affect the entire cornea, leading to painful blisters that may become infected. Other rarer pathologies, such as iridocorneal endothelial syndrome, posterior polymorphous corneal dystrophy, and congenital hereditary endothelial dystrophy, present unique diagnostic and therapeutic challenges. Additionally, graft failure remains a notable indication for treatment. High-risk cases experience failure rates exceeding 35% within 3 years, with endothelial rejection accounting for half of the cases. Corneal transplants have been considered the gold standard for decades, with advancements in surgical techniques leading to shorter operating times, faster visual recovery, and improved outcomes. However, the growing global shortage of transplant-grade donor tissue further complicates treatment, underscoring the urgent need for innovative approaches such as genetic and cell-based therapies.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".