Nomogram to Predict Graft Thickness in Descemet Stripping Automated Endothelial Keratoplasty: An Eye Bank Study
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to develop a nomogram to predict postcut thickness of corneal grafts prepared at an eye bank for Descemet stripping automated endothelial keratoplasty (DSAEK). METHODS: Retrospective chart review was performed of DSAEK graft preparations by 3 experienced technicians from April 2012 to May 2017 at the Eye Bank of Canada-Ontario Division. Variables collected included the following: donor demographics, death-to-preservation time, death-to-processing time, precut tissue thickness, postcut tissue thickness, microkeratome head size, endothelial cell count, cut technician, and rate of perforation. Linear regression models were generated for each microkeratome head size (300 and 350 μm). RESULTS: A total of 780 grafts were processed during the study period. Twelve preparation attempts resulted in perforation (1.5%) and were excluded. Mean precut tissue thickness was 510 ± 49 μm (range: 363-670 μm). Mean postcut tissue thickness was 114 ± 22 μm (range: 57-193 μm). Seventy-nine percent (608/768) of grafts were ≤130 μm. The linear regression models included precut thickness and donor age, which were able to predict the thickness to within 25 μm 80% of the time. CONCLUSIONS: We report a nomogram to predict thickness of DSAEK corneal grafts prepared in an eye bank setting, which was accurate to within 25 μm 80% of the time. Other eye banks could consider performing similar analyses.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".