Predicting Long-Term Endothelial Cell Loss after Preloaded Descemet Membrane Endothelial Keratoplasty in Fuchs’ Endothelial Corneal Dystrophy: A Mathematical Model
Bibliographic record
Abstract
(1) Background: This study offers a biexponential model to estimate corneal endothelial cell decay (ECD) following preloaded “endothelium-in” Descemet membrane endothelial keratoplasty (DMEK) in Fuchs’ endothelial corneal dystrophy (FECD) patients; (2) Methods: A total of 65 eyes undergoing DMEK alone or combined with cataract surgery were evaluated. The follow-up period was divided into an early phase (first 6 months) and a late phase (up to 36 months). Endothelial cell count (ECC) and endothelial cell loss (ECL) were analyzed; (3) Results: The half time of the ECD was 3.03 months for the early phase and 131.50 months for the late phase. The predicted time-lapse interval to reach 500 cells/mm2 was 218 months (18.17 years), while the time-lapse interval to reach 250 cells/mm2 was 349 months (29.08 years). There was no statistically significant difference between the ECL in DMEK combined with cataract extraction and DMEK alone at 24 months (p ≥ 0.20). At the late phase, long-term ECL prediction revealed a lower ECC half time in patients undergoing DMEK combined with cataract surgery (98.05 months) than DMEK alone (250.32 months); (4) Conclusions: Based on the mathematical modeling, a predicted average half-life of a DMEK graft could reach 18 years in FECD. Moreover, combining cataract extraction with DMEK could result in excessive ECL in the long term.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".