Long-Term Corneal Endothelial Cell Counts After Penetrating Keratoplasty in Infants
Bibliographic record
Abstract
PURPOSE: To report long-term endothelial cell counts after penetrating keratoplasty (PKP) in infants. METHODS: The charts of all children who have undergone PKP in their first year of life between 1998 and 2013 at the Hospital for Sick Children, Toronto, Canada, were reviewed retrospectively. Patients who had a single successful transplant with a valid endothelial cell density (ECD) analysis postsurgery were included in the study. Donor ECDs were provided by a local eye bank. Specular microscopy images were taken at a variable interval after surgery using a noncontact specular microscope (ROBO, Konan; Konan Medical) and endothelial cell loss was calculated. In young children images were taken in the lateral decubitus position under general anesthesia. RESULTS: Twenty-one eyes of 16 patients were included in the study. Median patient age at the time of surgery was 2.0 months [mode, 2.0; interquartile range (IQR), 1.6-2.6 months] and median follow-up time was 49.0 months (IQR, 33.0-99.5 months). The most common indication for surgery was Peters anomaly, in 16 eyes (76.2%). Ten eyes had additional intraocular surgeries posttransplant. The median ECDs prekeratoplasty and at last follow-up were 2958 cells per square millimeter (IQR, 2807-3205 cells/mm) and 1307 cells per square millimeter (IQR, 946-1613 cells/mm) respectively, reflecting a median endothelial cell loss of 59.2% (IQR, 44.3%-68.8%). Iris adhesions to the graft-host junction were strongly associated with low final ECD (P = 0.01). CONCLUSIONS: Despite technical challenges and difficult postoperative care, pediatric keratoplasty is associated with a lower endothelial cell loss over time compared with that reported in adults after PKP.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".