Case Report: Remote Scleral Lens Fitting for High Toric Scleras in a Keratoconus Patient
Bibliographic record
Abstract
SIGNIFICANCE: Technology plays a crucial role in customizing scleral lenses and improving lens alignment, especially in challenging scleral shapes. In addition, remote fitting technology allows optometrists to extend their expertise globally, empowering patients to access to customized lenses without travel expenses. PURPOSE: The objective of this study was to document the difficulties encountered in fitting a scleral lens in a patient with keratoconus and pronounced scleral toricity. In addition, the study aimed to present the successful remote fitting achieved by using advanced technology. CASE REPORT: An Irish male patient diagnosed with keratoconus exhibited high scleral toricity. Generally, keratoconus eyes often exhibit significant scleral asymmetry associated with cone decentration and disease severity. Improperly aligned scleral lenses can lead to regional changes in scleral shape, lens decentration, discomfort, and visual disturbances. Indeed, previous scleral lens fits were unsuccessful because of these issues. Corneoscleral profilometry was acquired in Ireland and then used in Italy to design customized lenses, which were then delivered to the patient's optometrist in Ireland. The first lenses designed and delivered demonstrated excellent overall performance without requiring adjustments. CONCLUSIONS: This report highlights the importance of corneoscleral profilometry to increase efficiency and reduce lens reorders and chair time, and the remote fitting in overcoming barriers to accessing specialized lens fitting.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".