Long-Term Effectiveness of Scleral Lens Treatment in the Management of Keratoconus: A Systematic Review
Bibliographic record
Abstract
This systematic review evaluates the long-term effectiveness of scleral lenses in improving visual outcomes, patient satisfaction, and safety in patients with keratoconus. A systematic search of six databases (PubMed, Embase, Web of Science, Scopus, Cochrane Library, and MEDLINE) was performed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The inclusion criteria included studies with more than or equal to three months of follow-up, included a minimum of 20 keratoconus patients, published after year 2020 and outcomes reporting visual acuity, comfort or quality of life. Methodological quality was assessed using the Newcastle-Ottawa Scale and Murad et al. case series tool. Five studies (463 eyes) met the inclusion criteria. Scleral lenses consistently improved best-corrected visual acuity (BCVA) across studies, with visual gains from baseline logMAR 0.50-0.53 to post-treatment logMAR 0.08-0.09. Significant improvements in vision-related quality of life (National Eye Institute Visual Function Questionnaire (NEI-VFQ) scores) were observed in validated assessments. Complications were infrequently reported but included lens handling issues (10.4%-63%), fogging (58%), and physiological events (e.g., corneal epitheliopathy). Long-term follow-up indicated that 14.6% of the patients experienced worsening visual acuity due to keratoconus progression. Methodological quality was variable, with limitations including variable outcome reporting and limited follow-up duration. Scleral lenses provide substantial and sustained improvements in visual acuity and quality of life for keratoconus patients, particularly those with advanced disease. However, the evidence is limited by methodological shortcomings and a lack of long-term controlled studies. Future research should prioritise randomised trials with standardised reporting and longer follow-up to better assess complications and durability of outcomes.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".