Management of post-LASIK dry eye: a multicenter randomized comparison of a new multi-ingredient artificial tear to carboxymethylcellulose
Bibliographic record
Abstract
Purpose: To compare the efficacy and safety of a preservative-free, multi-ingredient formulation of carboxymethylcellulose 0.5%, hyaluronic acid 0.1%, and organic osmolytes (CMC-HA), to preservative-free carboxymethylcellulose 0.5% (CMC) in the management of postoperative signs and symptoms of dry eye following laser-assisted in situ keratomileusis (LASIK). Methods: This was a double-masked, randomized, parallel-group study conducted in 14 clinical centers in Canada and Australia. Subjects with no more than mild dry eye instilled CMC-HA or CMC for 90 days post-LASIK. Ocular Surface Disease Index © (OSDI; primary efficacy measure), corneal staining, tear break-up time (TBUT), Schirmer’s test, acceptability/tolerability surveys, and visual acuity were assessed at screening and days 2, 10, 30, 60, and 90 post-surgery. Safety analyses included all enrolled. Results: A total of 148 subjects (CMC-HA, n=75; CMC, n=73) were enrolled and assigned to receive treatment, and 126 subjects completed the study without any protocol violations. Post-LASIK, dry eye signs/symptoms peaked at 10 days. OSDI scores for both groups returned to normal with no differences between treatment groups at day 90 ( P =0.775). Corneal staining, Schirmer’s test, TBUT, and survey results were comparable. Higher mean improvements in uncorrected visual acuity were observed in the CMC-HA group at all study visits, reaching statistical significance at day 30 ( P =0.013). Both treatments were well tolerated. Conclusion: CMC-HA-containing artificial tears relieved post-LASIK ocular dryness as well as CMC alone, and demonstrated incremental benefit in uncorrected vision, with a favorable safety profile. Results support use of CMC-HA eye drops to reduce signs and symptoms of ocular dryness post-LASIK. Keywords: LASIK, dry eye, artificial tears, carboxymethylcellulose, hyaluronic acid, ocular surface disease
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".