Latanoprostene Bunod 0.024% Early Experience Program (LEEP): A Canadian Initiative for Open-Angle Glaucoma and Ocular Hypertension
Bibliographic record
Abstract
INTRODUCTION: Here we report the intraocular pressure (IOP) changes following treatment with latanoprostene bunod (LBN) 0.024% in patients in Canadian ophthalmology practices. METHODS: This real-world, open-label, 6-week, observational study collected data from 59 Canadian ophthalmologists and a total of 653 patients. Eligibility was open to all patients treated with LBN 0.024% instilled once daily for open-angle glaucoma or ocular hypertension. IOP was assessed prior to initiating LBN and after a planned 6 weeks of treatment. Patient demographics, prior treatment(s), IOP, and patient/physician satisfaction ratings were recorded. Subgroup analyses included (1) patients naïve to prior IOP-lowering medication (with or without prior selective laser trabeculoplasty [SLT]) initiating LBN; (2) patients switching from a pre-existing medication to LBN; and (3) patients adding LBN to existing medications. RESULTS: Of 653 patients included, 251 were naïve to previous medical antihypertensive therapy, 369 were switched to LBN from a previous medication, and 26 added LBN on top of existing medications (seven patients did not indicate status). Mean baseline IOP was 19.5 mmHg in the overall cohort and follow-up occurred over a period of 37.9 ± 7.9 days. Mean IOP was reduced by 16.3% (95% confidence interval 14.9-17.7) across all included patients. Overall IOP reduction from baseline was largest for naïve patients with no prior SLT (29.3%), with age and baseline IOP key determinants of outcomes in this group. Patient and physician satisfaction scores were high. CONCLUSION: This initial Canadian clinical experience of LBN in diverse patient and physician populations reflected its use in a real-world context, and demonstrated a significant IOP-lowering effect with LBN across patient groups seen in routine practice. Patient and physician satisfaction scores were high and in notable agreement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".