Short-Term Efficacy and Safety of Scleral Lenses in the Management of Severe Dry Eye in a Chinese Population
Bibliographic record
Abstract
Background: Scleral lenses (SLs) are recommended in DEWS II to treat dry eye (DE) patients that do not respond well to conventional therapies. This study aimed to evaluate the short-term (one month) efficacy and safety of SLs in the management of severe DE. Methods: This single-center prospective study enrolled 15 patients (22 eyes) who were diagnosed with severe DE. The Ocular Surface Disease Index (OSDI), the Chinese version of the 25-item National Eye Institute Visual Function Questionnaire (CHI-VFQ-25), and LogMAR best-corrected visual acuity (BCVA) were evaluated at baseline and one month following SL fitting. DE-related parameters were obtained and analyzed before and after one month of SL treatment, including tear-film breakup time (TBUT), corneal fluorescein staining (CFS), non-invasive breakup time (NIBUT), tear meniscus height (TMH), Schirmer I test (SIT), and meibomian gland (MG) dropout. Complications and adverse events were monitored. Results: OSDI scores (53.9 ± 28.1 vs. 10.4 (4.2–25), p = 0.0001) and CFS scores (10.2 ± 3.9 vs. 7 (0–12), p = 0.001) decreased after one month of SL therapy, while CHI-VFQ-25 scores (74.4 (54.8–83.8) vs. 95 (78.7–98), p = 0.0001) and TBUT (0.6 ± 0.5 vs. 2.2 ± 1.0, p < 0.0001) increased significantly. LogMAR BCVA improved from 0 (0–0.1) to 0 (0–0) (p = 0.0147). The average types of medications per eye decreased from 2.82 ± 1.01 to 1.32 ± 0.64 (p = 0.025), and the proportion of eyes using glucocorticoids significantly decreased from 63.6% to 13.6% (p = 0.001). No severe SL-related adverse events were reported. Conclusions: SL treatment quickly alleviated subjective symptoms as well as clinical signs of DE with good safety and enhanced the visual function and vision-related quality of life, showing its usefulness in the management of severe DE.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.000 | 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".