Treatment of dry eyes with lifitegrast 5% before cataract surgery: A prospective trial
Bibliographic record
Abstract
Abstract Objective: The objective of the study was to evaluate the impact of dry eye treatment on intraocular lens (IOL) biometry measurements and postoperative refractive outcome. Design: This study involves a prospective interventional study. Participants: Consecutive patients with dry eye disease undergoing cataract surgery were included in the study. Materials and Methods: All participants underwent a comprehensive dry eye assessment including the ocular surface disease index (OSDI) questionnaire, Schirmer’s test without anesthesia, corneal fluorescein staining, and conjunctival lissamine green staining. Optical biometry was performed using swept-source optical coherence tomography (IOL Master ® 700, Zeiss), and corneal aberrometry was measured using a scanning-slit aberrometer (OPD SCAN III, Nidek). Diagnosis of dry eye was made according to Dry Eye Workshop II. Patients received 6 weeks of treatment of lifitegrast 5% and preservative-free artificial tear eye drops and had repeat testing at the end of 6 weeks. The change in subjective and objective dry eye tests and biometry measurements and the difference in target and postoperative refraction before and after treatment were compared. Results: Forty eyes of 21 patients were included in the study. Subjectively, OSDI improved from 26.35 ± 6.24 at baseline to 20.97 ± 8.41 after treatment ( P = 0.03). Objectively, corneal staining improved from 1.42 ± 1.78 at baseline to 0.39 ± 0.67 after treatment ( P = 0.006). No changes were observed in Schirmer’s test without anesthesia, conjunctival staining, or corneal aberrometry ( P > 0.05). Repeated biometry after dry eye treatment showed a change in IOL power difference ≥0.5 diopters in 50% (20) of eyes. Mean absolute error was within 0.25D in 87.5% (35) of eyes after treatment compared to 67.5% at baseline ( P = 0.05). Conclusion: Dry eye treatment leads to subjective symptom improvement and changes in IOL power calculations and postoperative refractive outcomes. Assessing and treating patients for dry eyes before cataract surgery is important in maximizing refractive 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".