Keratoscope-guided opening of graft-host junction to treat elevated astigmatism after deep anterior lamellar keratoplasty
Bibliographic record
Abstract
AIMS: To describe a simple surgical technique consisting of opening the vertical graft-host junction (GHJ) to manage high astigmatism following deep anterior lamellar keratoplasty (DALK) and to report postoperative visual and topographic outcomes. METHODS: This is a retrospective interventional case series. Patients affected by high astigmatism after DALK underwent progressive opening of the previous GHJ at the steep meridians, established with preoperative topography and intraoperative keratoscopic guidance. Evaluations were carried out with best corrected visual acuity (BCVA), refractive astigmatism (RA) and keratometric astigmatism (KA) measured with Casia (Tomey, Japan). Visits were conducted preoperatively (T0), 1 week (T1), 6 months (T2) and 1 year after surgery (T3). The Alpins Method was used to evaluate vectorial changes in KA between T0 and T3. RESULTS: Fifteen patients were included in the analysis. BCVA (median (IQR)) increased significantly from 0.70 (0.5-1.1) at T0 to 0.20 (0.1-0.3) logMAR at T3 (p=0.001). RA (mean±SD) at T3 was 2.28±1.59 dioptres (D). KA (mean±SD) varied significantly from 8.04±2.14 D at T0 versus 2.93±1.94 D at T1 versus 3.15±1.79 D at T2, versus 3.23±1.63 D at T3 (p<0.0001). Vectorial analysis showed that target induced astigmatism (mean±SD) was 8.04±2.14 D, while surgically induced astigmatism (mean±SD) was 7.89±4.27 D. Correction index (mean±SD) was 0.92±0.33. CONCLUSIONS: High astigmatism after DALK can be safely and effectively managed by opening the GHJ down to the cleavage plane achieved with the previous surgery, providing low KA and RA, with an overall low tendency to overcorrection.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".