Timeframe to Keratoplasty After Ocular Surface Stem Cell Transplantation
Bibliographic record
Abstract
PURPOSE: The aim of this study is to report the actual time delay between ocular surface stem cell transplantation (OSST) and keratoplasty at a high-volume center for limbal stem cell deficiency (LSCD). Understanding expected timelines and reasons for any delays or deferrals can allow for more accurate expectation-setting discussions with prospective patients. METHODS: A retrospective chart review was performed on all patients having undergone OSST between January 2013 and December 2022. Charts were reviewed to determine whether the need for keratoplasty was specified at the time of OSST planning. Parameters for these cases were then collected including underlying diagnoses, type of OSST performed, and time delay between OSST and keratoplasty (if performed). Results were then aggregated, and subgroup analysis was conducted. RESULTS: A total of 219 eyes were included, of which, 128 eyes had initial intent for keratoplasty. Of these, 48% had keratoplasty within 6 months of OSST, 17% between 6 and 24 months, 5% beyond 2 years, and, interestingly, 30% had no subsequent keratoplasty. Most eyes with no eventual keratoplasty experienced unanticipated stromal clearing after OSST obviating the need for keratoplasty. CONCLUSIONS: This is the first study to report realized time delay between OSST and keratoplasty at a large LSCD practice. Prospective patients can be counselled that 76% of compliant cases with initial intent for keratoplasty either had keratoplasty within 6 months or exhibited unanticipated stromal clearing after OSST alone. Patients with congenital aniridia or contact lens-related LSCD are more likely to have unanticipated stromal clearing.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".