Retreatment to Enlarge Small Excimer Laser Optical Zones Using Combined Myopic and Hyperopic Ablations
Bibliographic record
Abstract
PURPOSE: We describe a retreatment technique using a combination of large-diameter myopic and hyperopic excimer laser ablations of a near equivalent diopter value to enlarge previous small ablation zones without altering the refractive result obtained by the initial surgery. METHODS: Eight eyes of six patients were retreated with a Technolas Keracor 217 laser in order to enlarge the optical zone. All patients had a good refractive result after the initial surgery, but reported halos under low light conditions. Retreatment consisted of a combination of myopic and hyperopic ablations of near equivalent value, eg, -1.00 D and +1.00 D treatment. These opposite value ablations should neutralize their respective refractive effect, but by removing a larger diameter of tissue, actually displace the transition zones further toward the corneal periphery. Although the initial treatment used photorefractive keratectomy in four of the eight eyes, LASIK was used for retreatment in all eyes. Improvement was assessed subjectively by the patient and was measured objectively with a Technomed C-Scan ray-tracing program. RESULTS: In six of eight eyes, patients experienced moderate or marked improvement. This improvement was objectively correlated on postoperative corneal topography. CONCLUSION: Combined larger diameter myopic and hyperopic retreatment seems to be safe and effective for enlarging previous small treatment zones, reducing symptoms such as halos in selected patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".