Bibliographic record
Abstract
The 2024 three-year plan for Optometry & Vision Science included increasing the number of feature issues,1 and the journal has already published four high-level and well-received feature issues on: Advances in Vision Impairment Research in June 2024. Advances in Ocular Surface Research in September 2024 Aging, the Eye and Vision System in February 2025 Advances in Refractive Error Research in May 2025. We are excited to say that our next feature issue is on advances in glaucoma research. The world of glaucoma research continues to move quickly. Over the last 25 years, we have seen the redefinition of glaucoma as a neurodegenerative disease, imaging of retinal changes in previously unimaginable detail, have redefined normal tension glaucoma, improved functional testing, have new clinical guidelines, developed new pharmacological, laser, and surgical treatments, have new ways to measure intraocular pressure and examine the anterior chamber angle, and have seen the dawn of artificial intelligence, home monitoring, and personalized medicine. Especially pertinent to Optometry & Vision Science, in many parts of the world, we have seen optometrists take a leading role in the treatment and management of glaucoma, expanding and enhancing care provision. There is a great excitement that the next decade will see current research in neuroinflammation, neuroprotection, and neurorestoration lead to cures and eventually sight restoration. Boosting mitochondrial function, helping the retina to heal itself, clinical imaging at the cellular level, and tailoring interventions to the type and stage of disease are all approaches offering hope to our patients. These advances sit alongside ever-improving diagnostic and monitoring techniques, improved care pathways, and better understanding of how glaucoma affects vision and the daily activities of those with the disease. We invite you to join us in ensuring that the feature issue on advances in glaucoma research will disseminate, educate, and help prepare us all to understand the progress that is shaping our future clinical impact. DEADLINE The deadline for submission of papers to this feature issue is November 3, 2025. We are happy to accept submissions after the deadline, but late submissions requiring major revisions may not complete the peer review process in time for inclusion in the feature issue. Of course, if a slightly late submission received a “minor revision” decision, it would very likely be published in the feature issue. PUBLICATION ONLINE BEFORE THE ISSUE Each individual paper is published online quickly after acceptance and appears in the pre-publication area of Optometry & Vision Science’s webpages (https://journals.lww.com/optvissci/toc/pre-publication). The feature issue itself will then likely be published in the May or June 2026 issue. TOPICAL AREAS Our team of feature issue editors has highlighted several areas that we would particularly keen to receive submissions in. These include: AI and glaucoma Bioinformatics and glaucoma Diagnostics and disease detection Evaluating progression and disease management Innovative case series Disease mechanisms Clinical psychophysics, including perimetry Imaging (including Optical Coherence Tomography, Optical Coherence Tomography Angiography, adaptive optics, and oximetry) Animal models Cell and molecular biology Neuroinflammation, neuroprotection, and neuroregeneration Quality of life and task performance THE FEATURE ISSUE EDITORS The glaucoma research feature issue team is: Jonathan Denniss, University of Bradford, Bradford, United Kingdom, lead co-editor. John Flanagan, University of California at Berkeley, Berkley, California, lead co-editor. Bang Bui, The University of Melbourne, Victoria, Australia. Brad Fortune, Devers Eye Institute, Portland, Oregon. Danica Marrelli, University of Houston, Houston, Texas. Nimesh Patel, University of Houston, Houston, Texas. Jeremy Sivak, University of Toronto, Toronto, Canada. Joanne Wood, Queensland University of Technology, Queensland, Australia. Please pass on this call for papers to colleagues who may be interested in submitting papers. Please prepare submissions according to the Instructions for Authors: https://edmgr.ovid.com/ovs/accounts/ifauth.htm and submit them online at https://www.editorialmanager.com/ovs/default2.aspx, noting that your paper is being submitted for consideration of this feature issue.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.219 | 0.139 |
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".