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Record W4406765554 · doi:10.3390/jcto3010002

A Review of Ocular and Systemic Side Effects in Glaucoma Pharmacotherapy

2025· review· en· W4406765554 on OpenAlexaff
Xiaole Li, Michael Balas, David J. Mathew

Bibliographic record

VenueJournal of Clinical & Translational Ophthalmology · 2025
Typereview
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsPharmacotherapyGlaucomaMedicineOphthalmologyInternal medicine

Abstract

fetched live from OpenAlex

Glaucoma, the second leading cause of irreversible blindness globally, encompasses a heterogeneous group of ocular disorders characterized by the progressive degeneration of retinal ganglion cells. Pharmacotherapy remains the cornerstone of treatment, primarily aimed at reducing intraocular pressure (IOP) by decreasing aqueous humor production or enhancing its outflow. The therapeutic classes employed include carbonic anhydrase inhibitors, β-blockers, α-adrenergic agonists, prostaglandin analogs, parasympathomimetics, Rho kinase inhibitors, and hyperosmotic agents. Despite their efficacy, these medications are associated with a range of ocular and systemic side effects, influenced by their mechanisms of action, formulation, and dosage. Ocular adverse effects, such as irritation, dry eye, allergic reactions, and infections, are common, while systemic absorption may lead to more severe outcomes, including organ dysfunction, exacerbation of comorbid conditions, or life-threatening cardiovascular events. Given these potential risks, it is critical for clinicians to understand and monitor these adverse effects as they significantly affect patient adherence, quality of life, and treatment outcomes. Ongoing research is essential to develop novel therapeutic regimens, agents, or delivery methods that minimize side effects and improve compliance. Incorporating patient-reported outcomes in clinical practice may further enhance the assessment of treatment impact, facilitating more tailored and effective management of glaucoma.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.428
Threshold uncertainty score0.927

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.058
GPT teacher head0.469
Teacher spread0.411 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations12
Published2025
Admission routes1
Has abstractyes

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